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With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively. [ABSTRACT FROM AUTHOR]

We consider end-to-end live video streaming in mobile edge networks with fluctuating but predictable throughput. We employ scalable video coding (SVC) to adapt the bitrate of each frame to the throughput evolution in a timely manner. When streaming SVC-encoded live video, the video source should dynamically derive and configure a set of coding parameters for each upcoming chunk, which consists of a number of consecutive frames. The coding parameters quantify the throughput evolution of an upcoming chunk by providing a few bitrate options for the frames within this chunk to encode with. Unlike the majority of existing works that simply treat the coding parameters as given (i.e., taking them for granted), in this paper, we derive the coding parameters from a comprehensive mathematical modeling and optimization perspective. Our approach consists of an optimization problem that captures the fundamental trade-off in determining the coding parameters and a customized search algorithm based on the analysis of the optimization problem's solution structure. Numerical results produced by the NS-3 network simulator show that our approach outperforms the state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

This article investigates the reliability gain of broadcast (BC)/broadband (BB) cooperation for mobile scalable video streaming. A specific form of BC-BB cooperation is introduced, which is referred to as cooperative casting. The proposed system primarily utilizes a BC transmission for streaming, while BB backup transmissions are requested whenever the BC signal is not decodable. For BC deliveries in this system, a cross-layer collaboration between scalable video coding (SVC) and layered-division multiplexing is used to improve spectral efficiency by coupling their hierarchical structures. In this end, in tandem with designing the uplink requesting rules based on channel status, delivery performance of the cooperative casting is analytically evaluated in a probabilistic framework. The system design and analyses presented in this article focus on the service continuity in a mobile and time-varying channel, where the physical aspects, such as delayed unicast response and inter-carrier interference, are carefully taken into account. Compared to the standalone network transmissions, the cooperative casting is numerically verified to better prevent the delivery failures in high-speed mobile channels, especially when the SVC layer-combined (full-scale video) service is intended. Furthermore, it is also shown that relaxing the criteria for the BB stream requesting can reduce delivery failures substantially, while it increases transmission redundancy. [ABSTRACT FROM AUTHOR]

Aided by scalable video coding, multirate multicast has become a promising technique of providing differentiated quality of experience (QoE) for massive numbers of video subscribers operating in heterogeneous channel conditions. Nevertheless, due to the time-varying nature of wireless channels and the subscribers’ diverse requirements, it is challenging to dynamically control the video rate in the light of the available radio resource to achieve the best QoE. To elaborate a little further, the time scale of resource scheduling is of short-term nature, which determines the short-term video quality variation, but from a service provider's perspective the design objective is to optimize the long-term QoE for all subscribers. Despite its importance, this problem has not been considered before. Explicitly, we formulated this problem as a time-averaged stochastic optimization problem which avoids the impact of both the short-term channel quality fluctuation and that of the video bitrates, whilst maintaining both inter- and intra- group fairness. The stratified structure of the problem inspires us to decompose it into a two-phase optimization: coarse grained assignment for each user group and fine grained assignment for each subgroup. We propose an adaptive multicast algorithm based on Lyapunov's optimization theory for solving this problem, by striking a compelling trade-off between the system's utility and its queue stability. We quantify the achievable performance of our proposed solution based on realistic video traces. [ABSTRACT FROM AUTHOR]

This paper presents a novel transmission scheme to support massive machine-type communications (MTC) devices sending very short packets for Internet-of-Things (IoT) applications. The proposed scheme, termed as pilot-less one-shot (PLOS) transmission, does not require the pilot signaling. The key idea behind PLOS is to encode information into the inter-block nonzero positions and intra-block nonzero positions of a sparse vector. In the receiver, we propose a deep neural network-based scheme, referred to as deep learning-based PLOS (DL-PLOS) to recover the nonzero positions of the sparse vector. From the simulations results, we demonstrate that PLOS is effective in the short packet transmission and DL-PLOS outperforms the conventional greedy algorithms. [ABSTRACT FROM AUTHOR]

The problem of adaptive multi-user scalable layered video transmission is considered in energy harvesting (EH) aided wireless communication systems. With the goal of improving the quality of video services while providing fairness amongst the users despite the random nature of both energy harvesting and the channel quality, we formulate our Scalable Video Coding (SVC) design as a Constrained Utility Function Maximization (CUFM) problem. The proportional fairness and playback smoothness of our design is guaranteed by maximizing the log-sum of the users’ video qualities, while satisfying the battery fullness constraint and video layer (quality) fluctuation constraint. By invoking the classical Lyapunov drift based optimization technique, we further decompose the CUFM problem into two parallel subproblems, i.e., a dynamic transmission power allocation problem and a dynamic layer selection problem. By solving these two subproblems, we derive a joint power allocation and video layer selection strategy for multi-user SVC video transmission. The theoretical performance bound of the proposed solution is also presented. Numerical simulations are conducted with real H.264 SVC video traces and the experimental results demonstrate the reduced playback interruption rate and layer switching rate compared to a heuristic algorithm ProNTO. The results also illustrate a tradeoff between the system's utility function and the playback smoothness experienced by the users. [ABSTRACT FROM AUTHOR]

The use of high-quality 360° panoramic video is booming in the video industry. However, existing schemes for smartphones suffer from significant bandwidth consumption as they transmit the entire panoramic views in very high resolutions. This demand for bandwidth becomes even more problematic when multiple adjacent smartphones compete to access the same content, which further challenges a wireless network’s capacity, and when the available bandwidth fluctuates much more than wired networks. In this paper, we propose a cooperative streaming scheme for tile-based 360° video using scalable video coding (SVC) to maximize a group of users’ quality of experience. We formulate an optimization problem to choose optimal downloading and sharing subsets from a set of all requested SVC layers of tiles to maximize the effective quality of the users’ viewport while meeting the feasibility of the bandwidth of heterogeneous networks. We then show that the problem is NP-hard and compose a heuristic approach. In the approach, we rank the SVC layers based on the aggregated group-level preference to guide the devices’ downloading and sharing activities. A prototype on the Android platform is developed to test the approach’s performance, and the real-world results show that our proposed scheme outperforms baseline alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cooperative multi-user video streaming system, termed GiantClient, for videos encoded using scalable video coding (SVC). The proposed system allows a group of users to watch a video on a single screen. The users, who may have different data plans from different carriers or different levels of energy, can collaborate to fetch the SVC-encoded video at high quality and avoid running into re-buffering. Using SVC, each layer of every chunk of the video can be fetched by only one of the cooperating users. Therefore, we formulate the streaming problem that obtains the quality and the fetching policy decisions as an optimization problem. The objective is to optimize a novel quality-of-experience metric that maintains a tradeoff between maximizing the quality of every chunk and ensuring fairness among all video chunks for the minimum re-buffering time. The problem is constrained with the available bandwidth, the chunk deadlines, and the imposed maximum contribution constraints by users. Moreover, we propose a low-complexity algorithm to solve the proposed optimization problem. A real implementation of the system with real SVC-encoded videos and real bandwidth traces reveal the robustness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Efficient dissemination of videos is an important problem for mobile telecom carriers. In this paper, to facilitate massive video dissemination, we study joint caching and multicasting for multi-quality videos encoded using two video encoding techniques, namely, scalable video coding (SVC) and HEVC or H.264 as in dynamic adaptive streaming over HTTP (DASH) respectively, in a large-scale wireless network. First, for each type of video, we propose a random caching and multicasting scheme, carefully reflecting the relationship between layers of an SVC-based video or descriptions of a DASH-based video. Then, for each type of video, we derive tractable expressions for the successful transmission probability in the general and high user density regions, respectively, utilizing tools from stochastic geometry. The analytical results reveal that in the high user density region, the marginal increase of the successful transmission probability with respect to the caching probability of a video with a certain quality reduces when the caching probability increases. Next, for each type of video, we consider the maximization of the successful transmission probability in the high user density region, which is a convex problem with an exceedingly large number of optimization variables. We propose a two-stage optimization method to obtain a low-complexity near-optimal solution by solving a relaxed convex problem and a related packing problem. The optimization results reveal the impact of the caching gain of a layer for an SVC-based video or a description for a DASH-based video on its caching probability. Finally, we show that the proposed solutions for SVC-based and DASH-based videos achieve significant performance gains over baseline schemes in the general and high user density regions, and demonstrate their respective operating regions, using numerical results based on real video sequences. [ABSTRACT FROM AUTHOR]

In this paper, a non-orthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast scheme for cellular networks is proposed. This scheme combines the successive video-layer decoding in SVC with the successive interference cancellation (SIC) in NOMA, which enables a further reduction of the bottleneck effect imposed by cell-edge user equipments (UEs). Aiming at maximizing the overall video quality experienced by UEs in multiple multicast groups, the resource allocation for multiple groups and the scalable multicast scheduling within each group are formulated as a joint mixed-integer nonlinear programming problem. The formulated optimization problem is decoupled into a multi-group resource allocation (MRA) problem and multiple independent intra-group scalable multicast scheduling (IGSMS) subproblems. To solve IGSMS subproblems, we propose an optimal recursive algorithm, for which the optimal transmit power for each layer of the superposition coding needed in each iteration is derived in a closed form. The MRA problem is optimally solved via a knapsack approach. Extensive numerical results demonstrate the improved performance of the proposed NOMA-enhanced SVC multicast scheme over several baseline schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider multi-quality multicast beamforming of a video stream from a multi-antenna base station to multiple single-antenna users receiving different qualities of the same video stream, via scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding as well as successive interference cancelation, we propose a layer-based multi-quality multicast beamforming scheme. To reduce the computational complexity, we also propose a quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality information of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding optimal beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the layer-based power minimization problem is the same as that of the quality-based power minimization problem, although the latter incurs a lower computational complexity. Next, we consider the optimal joint layer selection and quality-based multi-quality multicast beamforming design to maximize the total utility representing the satisfaction with the received video quality for all users under a given maximum transmission power budget, which is NP-hard in general. Based on the optimal solution of the quality-based power minimization problem, we develop a greedy algorithm to obtain a near optimal solution. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions. [ABSTRACT FROM AUTHOR]

We investigate the near-optimal video layer placement in cache-enabled small-cell networks to provide on-demand video services with differential perceptual experiences. By employing scalable video coding, each video file is encoded into multiple layers, which are then stored in the local cache of small-cell base stations (SBSs). Based on the proposed caching scheme, we define the concept of average offloaded traffic (AOT), which quantitatively indicates the total amount of data traffic that can be sourced from the local cache of SBSs. Relying on stochastic geometry, we derive the expression for the successful transmission probability, based on which the closed form of AOT is obtained. Afterwards, under the cache size constraint of each SBS, the AOT maximization problem is formulated. Due to the NP-hard property of this integer programming problem, it is first relaxed into the convex form. Then, a greedy-strategy based heuristic algorithm is proposed, which can yield the near-optimal layer caching indicators. Numerical results validate our derivations and demonstrate that the proposed layer caching scheme outperforms the benchmark in terms of the AOT performance. [ABSTRACT FROM AUTHOR]

Mobile broadcast/multicast video services have become highly demanded in mobile networks and require effective and low-complexity radio resource management (RRM) strategies. This paper proposes an RRM strategy based on multicast subgrouping and the scalable video coding (SVC) technique for multicast video delivery that focuses on reducing the search space of solutions and optimizes the aggregated data rate (ADR). The results in terms of ADR, spectral efficiency, and fairness among multicast users, along with the low complexity of the algorithm, show that this new scheme is adequate for real systems. [ABSTRACT FROM AUTHOR]

SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality. [ABSTRACT FROM PUBLISHER]

Scalable video coding (SVC) is a coding paradigm that allows once-encoded video content to be used in diverse scenarios. SVC-coded videos can be transmitted and rendered at specified bitrates according to network bandwidth and end device requirements. In this paper, an adaptive bit allocation algorithm is proposed for the emerging scalable High Efficiency Video Coding (SHVC) standard. The bit budget at the group-of-pictures level is allocated according to buffer occupancy. Picture complexity, measured using the predicted mean absolute difference (MAD), buffer occupancy, and hierarchical level, is proposed for regulating the bitrate at the picture level. The MAD of the current picture is predicted using a novel mean prediction error (MPE) model, which is obtained from the advanced motion vector prediction, and the test zone search specified in SHVC and the associated reference software of SHVC. Moreover, MPE is used to determine the number of assigned bits at the coding-tree-unit level. The bit budget of each level is incorporated with the $R-\lambda $ model for computing the required quantization parameter. Experimental results reveal that the proposed method achieves accurate bitrates with enhanced and consistent visual quality and more satisfactorily controls buffer occupancy compared with the state-of-the-art approaches reported in the literature. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel method for layered media transmission in mobile broadcast systems, which is referred to as the layer-mixed forward error correction (FEC). Unlike the existing unequal error protection and layer-aware FEC which decode each layer symbols with a probability, the new approach is capable of decoding each layer data deterministically as long as enough symbols are received corresponding to the specific layer. The lower layer is always decoded prior to upper layers, which facilitates the delivery of scalable media stream, especially in mobile TV services with heterogeneous networks and terminals. In order to evaluate the performance fairly, this paper compares the minimum channel capacity required by several FEC schemes. The experimental results show that the new approach outperforms the existing schemes in severely degraded channel situations. [ABSTRACT FROM PUBLISHER]

This paper focuses on the transmission of layered source information, such as scalable video coding, over heterogeneous cellular networks. Scalable transmission enables dynamic adaption of source information to the condition of user equipment (UE) devices and, thus, is suitable for cellular networks in which the transmission link quality substantially varies over space and time. Two novel transmission schemes are proposed, namely, layered digital transmission and layered hybrid digital–analog (LHDA) transmission. Leveraging tools from stochastic geometry, a comprehensive analysis is conducted focusing on three key performance metrics: outage probability, high-definition probability, and average distortion. The results show that both proposed transmission schemes can provide a scalable video experience for UE devices. For LHDA transmission, the optimal power allocation between digital and analog transmissions is also analyzed. When the proportion of frequency resource allocated to the femtotier exceeds a certain threshold, LHDA transmission is preferable by enabling continuous quality scalability, thus avoiding the cliff effect. [ABSTRACT FROM PUBLISHER]

We propose a joint rate and power adaptation scheme to maximize the decoding quality for scalable video coding (SVC)-based video transmissions over multi-input multioutput (MIMO) systems. The rate adaptation in our proposed scheme includes selection of the best modulation and coding schemes, set of spatial channels, number of SVC layers (source coding rates), and their corresponding application layer forward error correction (APP-FEC) coding rates. The power adaptation involves the proper allocation of the power to each antenna in the MIMO system. SVC-based video transmissions require unequal error protection (UEP) for different SVC layers due to the inter-layer dependency. In most of the previous works, the bit stream of each particular SVC layer is allocated to one spatial channel and the UEP is achieved by transmitting the more important SVC layers through the spatial channels with higher channel gains. However, in our proposed scheme, the bit stream of each particular SVC layer is distributed to multiple spatial channels so that additional diversity gain can be exploited by applying APP-FEC. The UEP can also be achieved by allocating different APP-FEC coding rates on each video layer. Moreover, transmit power allocation is also effectively and jointly determined to improve the system performance. The effectiveness and favorable performance of our proposed scheme are shown by simulations with H.264 SVC traces of high-definition video clips over MIMO systems. [ABSTRACT FROM AUTHOR]

Multi-hop relaying combined with scarcity of wireless resources in ad-hoc networks can deteriorate the quality of service. As a result, one of the major challenges in video streaming over ad-hoc networks is enhancing users' experience and network utilization. The emergence of scalable video coding standard enables smooth adaptation of video quality to network conditions. In this paper, we study two optimization problems: (1) maximize the global quality of experience of all users and (2) maximize the number of qualified streams. We formulate the both problems as mixed integer linear programming problems. These optimization problems are shown to be NP-hard. Consequently, we propose heuristic algorithms to solve them. Simulation results show that the proposed algorithms can provide the near-optimal video quality while the calculation times are much shorter than the one of optimal solution. [ABSTRACT FROM AUTHOR]

In mobile systems, it is essential to limit the amount of energy consumption if the energy budget is low. This paper proposes a new scheme that makes use of quality scalability in scalable video coding (SVC) to limit energy usage by taking quality-of-experience (QoE) factors into account. To address the tradeoff between QoE and energy consumption, a new dynamic voltage and frequency scaling (DVFS) technique is proposed in which the fidelity level of each video segment is dynamically selected. The proposed scheme estimates the amount of energy consumption based on the analysis of the computational requirements to decode previous frames, and chooses the fidelity level to achieve the best video quality metric (VQM) value within a given energy budget. It was implemented on a recent commercial smartphone in which a new interface is provided for users to control the energy usage based on video quality. Real measurements show that it makes effective use of the remaining battery while maximizing QoE1. [ABSTRACT FROM PUBLISHER]

Scalable Video Coding (SVC) has been raised as a promising technique to enable flexible video transmission for mobile users with heterogeneous terminals and varying channel capacities. In this paper, we design an adaptive layer switching algorithm for on-demand scalable video service based on receiver’s buffer underflow probability (BUP). Since the low quality of channel may lead to a low buffer fullness, the buffer fullness is an indicator for reflecting the channel condition and we define BUP for characterizing the mismatch between the video bitrate and the channel throughput. Accordingly, the adaptive SVC transmission problem is formulated as the adaptive adjustment of video layers based on BUP. This allows us to optimize the attainable video quality, while keeping BUP below a desired level. To estimate BUP, we derive an analytical model based on the large deviation principles. Then, an online layer switching algorithm is proposed using this estimation model, which is capable of accommodating different channel qualities without any prior knowledge of the channel variations and of the video characteristics. We further introduce a perturbation-based layer switching approach for reducing the quality fluctuating issue caused by frequent layer switches, thus improving the viewer’s quality of experience. A system prototype is implemented to evaluate the success of the proposed method. We also conduct simulations in multiuser scenarios with real video traces and the results demonstrate that the proposed algorithm is capable of improving the playback experience, while keeping a low playback interruption rate and quality variation. [ABSTRACT FROM PUBLISHER]

The growing demand for mobile multicast services, such as Internet Protocol television (IPTV) and video streaming, requires effective radio resource management (RRM) to handle traffic with strict quality-of-service constraints over Long-Term Evolution (LTE) and beyond systems. Special care is needed to limit system performance degradation when multiple multicast streams are simultaneously transmitted. To this aim, this paper proposes an RRM policy based on a subgrouping technique for the delivery of scalable multicast video flows in a cell. Our proposal enhances the legacy multicast transmission over LTE systems by exploiting multiuser diversity and the users' channel quality feedbacks. Moreover, it is designed to take advantage of the frequency selectivity in the subgroup formation. Simulation results demonstrate the effectiveness of the proposed scheme, which outperforms existing approaches from the literature. It succeeds in achieving higher spectral efficiency and guaranteeing adequate video quality to all multicast receivers and improved quality to those with good channel conditions. [ABSTRACT FROM AUTHOR]

This paper provides an overview of Scalable High efficiency Video Coding (SHVC), the scalable extensions of the High Efficiency Video Coding (HEVC) standard, published in the second version of HEVC. In addition to the temporal scalability already provided by the first version of HEVC, SHVC further provides spatial, signal-to-noise ratio, bit depth, and color gamut scalability functionalities, as well as combinations of any of these. The SHVC architecture design enables SHVC implementations to be built using multiple repurposed single-layer HEVC codec cores, with the addition of interlayer reference picture processing modules. The general multilayer high-level syntax design common to all multilayer HEVC extensions, including SHVC, MV-HEVC, and 3D HEVC, is described. The interlayer reference picture processing modules, including texture and motion resampling and color mapping, are also described. Performance comparisons are provided for SHVC versus simulcast HEVC and versus the scalable video coding extension to H.264/advanced video coding. [ABSTRACT FROM PUBLISHER]

Providing scalable video streaming services for heterogeneous users in dynamic networked environments requires efficient and adaptive quality management mechanisms which deliver quality-customized services according to the client’s preferences and adapt the services to cope with various network conditions. In this paper, we address the issue of quality adaptation for providing personalized scalable media streaming services in dynamic network environments. We propose a differentiated adaptive quality optimization algorithm, called Scalable Video Coding Quality Adaptation algorithm (SVC-QA), which adapts streaming quality based on both system-level and client-level optimization to optimize streaming quality according to network bandwidth conditions, content characteristics, a user’s quality preferences, and buffering capacities of different client devices (e.g., mobile phones, PCs, HDTVs, etc.). Comparative studies are conducted to compare our proposed algorithms with other adaptive methods. We show that two-level SVC quality adaptation method can achieve better SVC streaming quality with both high peak signal-to-noise ratio (PSNR) and low quality variance under dynamic resource constraints. Moreover, the proposed distributed method reduces the computational complexities at the server side substantially, making it practical and flexible for providing scalable streaming services. [ABSTRACT FROM AUTHOR]

We propose an improved coarse-grain scalable video coding (SVC) approach based on the structural similarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers. First, we develop an interlayer rate-SSIM dependency model, by investigating bit rate and SSIM relationships between different layers. Second, a reduced-reference SSIM-Q model and a Laplacian R-Q model are introduced for SVC, by incorporating the characteristics of hierarchical prediction structure in each layer. Third, based on the user-defined weightings and the proposed models, we design a rate-distortion optimization approach to adaptively adjust Lagrange multipliers for all layers to maximize the overall rate-SSIM performance of the scalable encoder. Experiments with multiple layers, different layer weightings, and various videos demonstrate that the proposed framework can achieve better rate-SSIM performance than single layer optimization method, and provide better coding efficiency as compared to the conventional SVC scheme. Subjective tests further demonstrate the benefits of the proposed scheme. [ABSTRACT FROM AUTHOR]

Scalable Video Coding (SVC) is an extension of H.264/Advanced Video Coding (AVC), which has good characteristics for video transmission over networks. SVC encodes a video into a base layer (BL) and multiple enhancement layers (ELs). With the degree of importance of each layer, in addition to different modulation and coding schemes (MCSs), we can assign different retry attempt limits to each layer to improve quality of experience (QoE) metrics, such as average playback bitrate and buffering ratio. For example, we can assign a slower MCS and a higher retry limit to the BL, to reduce the loss rate and maintain the playback smoothness; whereas, at the same time, we can also assign faster MCSs and lower retry limits to each of the ELs, to reduce the buffering ratio. In this paper, we present a QoE-based link adaptation (QLA) scheme for H.264/SVC video streaming over IEEE 802.11 b/g wireless LANs. We then present a multicast extension of the QLA scheme, multicast QLA (MQLA). We implemented our QLA and MQLA schemes on a Linux-based Wi-Fi protocol driver, mac80211, and built a testbed to conduct our experiments. Experiment results show that both our schemes exhibit an improved QoE performance over the default link adaptation scheme provided by the Linux wireless driver, minstrel. [ABSTRACT FROM AUTHOR]

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes. [ABSTRACT FROM PUBLISHER]

Scalable video coding (SVC) is a promising technology for video multicast to users with different available transmission capacities. On the other hand, adaptive modulation and coding (AMC) schemes provide an efficient way to better utilize wireless network resources. However, the resource-allocation problem to support SVC multicast over wireless relay networks is a challenging one due to the existence of relay stations (RSs). We simplify the resource-allocation problem in this paper by assuming that the modulation and coding schemes (MCSs) assigned to lower video layers have equal or better coverage area than that assigned to higher video layers at each base station (BS)/RS. We discuss the optimal solution to the simplified problem and its relation with the orienteering problem (OP). Then, we propose a tabu search (TS)-based metaheuristic algorithm for dealing with the problem, which has been proven to be an effective and efficient algorithm for OP. The simulation results reveal that the proposed algorithm outperforms other algorithms under the assumptions of two-hop wireless relay networks or separated transmissions for the relay and access links, and it maintains a good approximation to the optimal results. [ABSTRACT FROM PUBLISHER]

In the multiview video plus depth (MVD) coding format, both texture and depth views are jointly compressed to represent the 3-D video content. The MVD format enables synthesis of virtual views through depth-image-based rendering; hence, distortion in the texture and depth views affects the quality of the synthesized virtual views. Bit allocation between texture and depth views has been studied with some promising results. However, to the best of our knowledge, most of the existing bit-allocation methods attempt to allocate a fixed amount of total bit rate between texture and depth views; that is, to select appropriate pair of quantization parameters for texture and depth views to maximize the synthesized view quality subject to a fixed total bit rate. In this paper we propose a scalable bit-allocation scheme, where a single ordering of texture and depth packets is derived and used to obtain optimal bit allocation between texture and depth views for any total target rates. In the proposed scheme, both texture and depth views are encoded using the quality scalable coding method; that is, medium grain scalable (MGS) coding of the Scalable Video Coding (SVC) extension of the Advanced Video Coding (H.264/AVC) standard. For varying target total bit rates, optimal bit truncation points for both texture and depth views can be obtained using the proposed scheme. Moreover, we propose to order the enhancement layer packets of the H.264/SVC MGS encoded depth view according to their contribution to the reduction of the synthesized view distortion. On one hand, this improves the depth view packet ordering when considered the rate-distortion performance of synthesized views, which is demonstrated by the experimental results. On the other hand, the information obtained in this step is used to facilitate optimal bit allocation between texture and depth views. Experimental results demonstrate the effectiveness of the proposed scalable bit-allocation scheme for texture and depth views. [ABSTRACT FROM AUTHOR]

Video and multimedia streaming services continue to grow in popularity and are rapidly becoming the largest consumers of network capacity in both fixed and mobile networks. In this article we discuss the latest advances in video compression technology and demonstrate their potential to improve service quality for consumers while reducing bandwidth consumption. Our study focuses on the adaptation of scalable, highly compressed video streams to meet the resource constraints of a wide range of portable consumer devices in mobile environments. Exploring SHVC, the scalable extension to the recently standardized High Efficiency Video Coding scheme, we show the bandwidth savings that can be achieved over current encoding schemes and highlight the challenges that lie ahead in realizing a deployable and user-centric system. [ABSTRACT FROM PUBLISHER]

Memory bandwidth and on-chip memory requirements are critical issues in motion estimation (ME) implementations for video compression. The H.264/AVC scalable extension (SVC) provides variable frame rate and resolution video in a compressed digital sequence with interlayer prediction, which complicates the problems of limited memory bandwidth and on-chip memory size. In this paper, an ME algorithm is proposed for the hardware encoder design of SVC that meets memory bandwidth and on-chip memory requirements. Clustered motion estimation and coding sequence reordering at macroblock and frame level processing are proposed. Compared with existing algorithms, the proposed algorithm has a 49.20% lower external memory bandwidth and reduces the on-chip memory requirement by 80.45% with video quality enhancements of up to 0.087, 0.090, 0.078, and 0.070dB for four-layer (FullHD-HD-D1-CIF) spatial scalability, respectively. [ABSTRACT FROM PUBLISHER]

This paper presents a complexity-aware adaptive spatial preprocessing scheme for the efficient scalable video coding (SVC) by employing an adaptive prefilter for each SVC layer. According to the presented scheme, a dynamic transition region is defined between the region of interest (ROI) and background within each video frame, and then various parameters of each prefilter (such as the standard deviation, kernel matrix size, and also a number of filters for the dynamic preprocessing of a transition region between the ROI and the background) are adaptively varied. The presented scheme has proved to be very efficient because it is based on an SVC computational complexity-rate-distortion analysis, thereby adding a complexity dimension to the conventional SVC rate-distortion analysis. As a result, the encoding computational complexity resources are significantly reduced, which is especially useful for portable encoders with limited power resources. The performance of the presented adaptive spatial preprocessing scheme is evaluated and tested in detail from both computational complexity and visual presentation quality points of view, further comparing it with the joint scalable video model reference software (JSVM 9.19) and demonstrating significant improvements. [ABSTRACT FROM PUBLISHER]

H.264 scalable extension (H.264/SVC) is the current state-of-the-art standard of the scalable video coding. Its interlayer prediction provides higher coding efficiency than previous standards. Since the standard was proposed, several attempts have been made to improve the performance based on its coding structure. Quantization-distortion (Q-D) modeling is a fundamental issue in video coding; therefore, this paper proposes new Q-D models for three interlayer predictions in 264/SVC spatial scalability, that is, interlayer motion prediction, intraprediction, and residual prediction. An existing single layer offline Q-D model is extended to H.264/SVC spatial scalable coding. In the proposed method, the residual power from the interlayer prediction is decomposed into the coding distortion and the prediction distortion. The prediction distortion is the mean square error (MSE) between two original signals that can be obtained by preprocessing with low complexity. Therefore, the coding distortion can be estimated based on both the quantization parameter (QP) and a precalculated prediction distortion before the encoding process. Consequently, the estimated quality based on the proposed models achieved a high accuracy of over 90% for the three interlayer predictions in average. [ABSTRACT FROM PUBLISHER]

Consistent video quality control is an important and practical issue for video streaming applications. Several algorithms have been developed in the literature that aims to maintain consistent quality through the entire video sequence, whereas most of them focus on non-scalable video coding. In this paper, we propose an algorithm for consistent quality control for H.264/AVC based scalable video coding (SVC), relying on a dependent distortion-quantization (D-Q) model. Such a dependent D-Q model is developed to capture the distortion behavior of frames at enhancement layers (ELs) by exploring the correlation between two successive layers. Experimental results demonstrate that the proposed model can accurately estimate the distortion of each frame at different layers in the quality, temporal, and spatial scalability. The proposed model is applied in SVC where the quantization parameter of each frame at EL is carefully selected to achieve consistent video quality given the distortion constraint. Meanwhile, model parameters are initialized using the content features extracted from the underlying video sequences, and updated using the encoded data at the frame level. Simulations show that the proposed scheme enables more stable video quality with the PSNR keeping close to the target value (i.e., small PSNR variation). [ABSTRACT FROM PUBLISHER]

The video coding standard, H.264/AVC scalable video extension (SVC), adopts various advanced interlayer prediction modes to explore the data redundancies between layers for better coding efficiency but at the expense of significantly increased computational complexity and data access bandwidth, especially for hardware realization of fractional motion estimation mode decision. To deal with this problem, this paper proposes a mode preselection algorithm for fractional motion estimation in scalable video coding. We first analyze the rate distortion cost relationship between different prediction modes. With the statistical results, several mode preselection rules are proposed to filter out the potentially skippable prediction modes. Simulation results show that our proposed algorithm reduces up to 65.97% prediction modes and 79.79% coding time on average with only 0.036 dB and 0.496% BD-peak signal-to-noise ratio (PSNR) degradation and BD-rate increase, respectively. Furthermore, the proposed mode preselection algorithm has been implemented in hardware and it costs only 9 k gate counts, when synthesized by 90 nm CMOS technology. [ABSTRACT FROM PUBLISHER]

The high bit rates of high-definition or 3Dservices require a huge share of the valuable terrestrial spectrum, especially when targeting wide coverage areas. This article describes how to provide future services with the state-of-the-art digital terrestrial TV technology DVB-T2 in a flexible and cost-efficient way. The combination of layered media such as the scalable and 3D extension of the H.264/AVC or emerging H.265/HEVC format with the physical layer pipes feature of DVB-T2 enables flexible broadcast of services with differentiated protection of the quality layers. This opens up new ways of service provisioning such as graceful degradation for mobile or fixed reception. This article shows how existing DVB-T2 and MPEG-2 transport stream mechanisms need to be configured for offering such services over DVB-T2. A detailed description of the setup of such services and the involved components is given. [ABSTRACT FROM AUTHOR]

Transcoding of H.264/AVC bitstreams is frequently used for adapting video streams to varying network conditions or different end-user device characteristics. Transcoding requires a relatively high complexity (and thus high operational cost) for each adaptation operation, and possibly reduces the video quality. In comparison, transcoding the input bitstream to SVC requires only one adaptation step. Afterwards, the resulting SVC bitstream can be adapted instantly. To reduce the H.264/AVC-to-SVC transcoding complexity, a transcoding architecture which combines existing open-loop transcoding techniques with optimized closed-loop transcoding is proposed. Furthermore, this approach allows to scale the complexity of the system. Doing so, compared with a cascaded decoder-encoder only 8.48% of the complexity is required for the highest complexity, while a high rate-distortion efficiency is maintained and a high degree of scalability is guaranteed. When only limited resources are available, the transcoding complexity can be scaled such that over 99% of the complexity is reduced compared to a cascaded decoder-encoder. [ABSTRACT FROM PUBLISHER]

We investigate dependent rate/distortion (R/D) modeling techniques for H.264/SVC videos. We introduce a self-domain (S-domain) analysis method for characterizing the dependent R/D behaviors, where the R/D characteristics of a base layer are employed as the observation domain for those of dependent layers. Based on S-domain observations, we propose empirical dependent R/D models and analyze physical implications of the proposed models. As an application of the proposed R/D models, we examine a joint temporal-quality layer bit allocation algorithm formulated as a Lagrange optimization problem. The proposed R/D models enable us to derive an analytical solution to the joint optimization problem. Finally, it is demonstrated by experimental results that our bit allocation algorithm outperforms JSVM benchmark by a significant margin (10%–20%) at various bit rates. [ABSTRACT FROM PUBLISHER]

A fast mode decision algorithm for efficient implementation of the scalable video coding (SVC) extension of H.264/AVC is described. SVC incorporates interlayer prediction, a new tool that exploits as much lower layer information as possible in order to improve the coding efficiency of the enhancement layer. However, it also greatly increases the computational complexity. A fast mode selection algorithm that exploits the correlation of a macroblock in the enhancement layer and both the colocated macroblocks in the base layer and neighboring macroblocks in the enhancement layer is proposed. The algorithm examines the level of picture details and motion activity, and utilizes the mode information of the base layer to make faster enhancement layer decisions and thus save coding time. Simulation results show that the proposed algorithm reduces encoding by up to 84% compared with the JSVM 9.18 implementation. This is achieved without any noticeable degradation in rate distortion. [ABSTRACT FROM PUBLISHER]

The distribution of copyrighted scalable video content to differing digital devices requires protection during rendering and transmission. In this paper, we propose a complete security system for H.264/scalable video coding (SVC) video codec and present a solution for the bit-rate and format compliance problems by careful selection of entropy coder syntax elements (bin-strings) for selective encryption (SE), and the problem of managing multiple layer encryption keys for scalable video distribution. A standard key management protocol, multimedia Internet keying protocol, is implemented for the hierarchical key generation mechanism, in which a subscriber has only one encryption key to unlock all scalable layers that have been subscribed to. The evaluation demonstrates the resulting video quality degradation arising from SE for many CIF and 4CIF test video sequences, without there being any impact upon the bit-rate or format compliancy, and with small computational delay. The security and statistical analysis performed further verify the effectiveness of the proposed security system for H.264/SVC. The proposed system is highly suitable for video distribution to users who have subscribed to a varying degree of video quality on devices with medium to high computational resources. [ABSTRACT FROM PUBLISHER]

Spatially scalable video coding (SSVC) provides an efficient way to transmit one video at different resolutions. Based on the emerging High Efficiency Video Coding (HEVC), we propose an SSVC scheme to support both single-loop (SL) and multiloop (ML) solutions by enabling different interlayer prediction mechanisms. Specifically, we employ two interlayer prediction modes: quadtree-based prediction mode (Q-mode) and learning-based prediction mode (L-mode). The Q-mode is investigated to exploit the interlayer redundancy based on the quadtree coding structure of HEVC. Due to the high correlation between layers, Q-mode utilizes the coded information from the base layer quadtree, including coding unit split, prediction unit partition, motion information, and partial texture information of transform unit, to predict the enhancement layer quadtree. By enabling Q-mode, we provide a basic SL solution for low complexity applications. Besides the correlation explored in Q-mode, we employ an extra L-mode to further improve the coding performance. In L-mode, the temporal-spatial correlation is exploited simultaneously by visual patch-based learning and mapping at pixel level. This helps us achieve more accurate prediction signals based on the coarse base layer reconstruction within an ML structure. Experimental results show the effectiveness of our SSVC scheme compared with the simulcast case and other HEVC-based SSVC schemes. [ABSTRACT FROM PUBLISHER]

Designing a real-time video-streaming system involves the challenges of, network congestion and bandwidth fluctuations, lowering the video quality. In this paper, a realtime video-streaming system is designed and implemented for scalable video coding (SVC) with an adaptive control scheme, containing macro- and micro-control, to resolve the bandwidth fluctuation and network congestion. Under macro-control, the server allocates appropriated video-streaming of SVC for heterogeneous clients based on terminal capacity and network conditions. Under micro-control, the server changes the video frame rate based on video bit rates and network bandwidth. The streaming system with the adaptive control scheme is implemented to improve the peak signal noise ratio (PSNR) by 4.01 % to 10.89 % of the Foreman and Stefan videos. [ABSTRACT FROM AUTHOR]

Ultra-high definition (UHD) which has 4 to 16 times as many pixels as existing high definition (HD) is expected as a next generation video format. To deliver UHD and HD videos simultaneously in the communications-broadcasting convergence environment, scalable video coding (SVC) is a highly attractive solution. We propose an improved scalable video coding method to achieve high coding efficiency particularly for UHD and HD videos. The basic idea is to allow large block size beyond the block size of 16x16 pixels in H.264/AVC SVC, which results in more efficient inter-layer prediction and syntax elements coding. The experimental results show that it achieves an average 5.34% reduction in BD-rate relative to H.264/AVC SVC. [ABSTRACT FROM PUBLISHER]

Temporal scalability is supported in scalable video coding (SVC) by means of hierarchical prediction structures, where the higher layers can be ignored for frame rate reduction. Nevertheless, this kind of scalability is not totally exploited by the rate control (RC) algorithms since the hypothetical reference decoder (HRD) requirement is only satisfied for the highest frame rate substream of every dependence (spatial or coarse grain scalability) layer. In this paper, we propose a novel RC approach that aims to deliver several HRD-compliant temporal resolutions within a particular dependence layer. Instead of using the common SVC encoder configuration consisting of a dependence layer per each temporal resolution, a compact configuration that does not require additional dependence layers for providing different HRD-compliant temporal resolutions is proposed. Specifically, the proposed framework for rate-controlled SVC uses a set of virtual buffers within a dependence layer so that their levels can be simultaneously controlled for overflow and underflow prevention while minimizing the reconstructed video distortion of the corresponding substreams. This in-layer multibuffer approach has been built on the top of a baseline H.264/SVC RC algorithm for variable bit rate applications. The experimental results show that our proposal achieves a good performance in terms of mean quality, quality consistency, and buffer control using a reduced number of layers. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed robust optimization scheme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over practical wireless networks. In order to guarantee layered utility maximization, the initial nominal joint source and network optimization is defined, where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates with network coding. To enhance the robustness of the nominal convex optimization formulation with nonlinear constraints, we reserve partial bandwidth for backup paths disjoint from the primal paths. It considers the path-overlapping allocation of backup paths for different receivers to take advantage of network coding, and takes into account the robust multipath rate-control and bandwidth reservation problem for scalable video multicast streaming when possible link failures of primary paths exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters related to the wireless channel. The targeted uncertainty in the robust optimization problem is studied in a form of protection functions with nonlinear constraints, to analyze the tradeoff between robustness and distributedness. Using the dual decomposition and primal-dual update approach, we develop a fully decentralized algorithm with regard to communication overhead. Through extensive experimental results under critical performance factors, the proposed algorithm could converge to the optimal steady-state more quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes. [ABSTRACT FROM AUTHOR]

Although the temporal scalability coding supported by both H.264/AVC and H.264/SVC provides a good means of adapting bitstream scalability in temporal dimension, it entails heavy computational complexity due to bidirectional motion estimation and compensation for B-pictures in temporal scalability levels. To make it more applicable, this paper introduces a fast inter-prediction mode decision method which is efficiently performed using statistical hypothesis testing. The hypothesis testing is performed on mean and variance of pixels in 16 \times 16 and 8 \times 8 blocks to decide early termination against further processing for their sub-blocks. Fallback check is employed to compromise the tradeoff between the compression efficiency and encoding time saving. The proposed method exhibits effective performance for early termination in the inter-prediction mode decision, thus leading to a significant reduction of the total encoding time up to average 64.8% with small increments in bit amounts and small degradation in visual quality. [ABSTRACT FROM PUBLISHER]

A joint model of scalable video coding (SVC) uses exhaustive mode and motion searches to select the best prediction mode and motion vector for each macroblock (MB) with high coding efficiency at the cost of computational complexity. If major characteristics of a coding MB such as the complexity of the prediction mode and the motion property can be identified and used in adjusting motion estimation (ME), one can design an algorithm that can adapt coding parameters to the video content. This way, unnecessary mode and motion searches can be avoided. In this paper, we propose a content-adaptive ME for SVC, including analyses of mode complexity and motion property to assist mode and motion searches. An experimental analysis is performed to study interlayer and spatial correlations in the coding information. Based on the correlations, the motion and mode characteristics of the current MB are identified and utilized to adjust each step of ME at the enhancement layer including mode decision, search-range selection, and prediction direction selection. Experimental results show that the proposed algorithm can significantly reduce the computational complexity of SVC while maintaining nearly the same rate distortion performance as the original encoder. [ABSTRACT FROM PUBLISHER]

In this paper, we present a theoretical analysis of the distortion in multilayer coding structures. Specifically, we analyze the prediction structure used to achieve temporal, spatial, and quality scalability of scalable video coding (SVC) and show that the average peak signal-to-noise ratio (PSNR) of SVC is a weighted combination of the bit rates assigned to all the streams. Our analysis utilizes the end user's preference for certain resolutions. We also propose a rate–distortion (R–D) optimization algorithm and compare its performance with that of a state-of-the-art scalable bit allocation algorithm. The reported experiment results demonstrate that the R–D algorithm significantly outperforms the compared approach in terms of the average PSNR. [ABSTRACT FROM PUBLISHER]

Multiple description coding (MDC) decomposes one single media into several descriptions and transmits them over different channels for error resilience. Each description contributes to improving the reconstructed media quality when decoded. Distributed video coding (DVC) encodes multiple correlated images and utilizes error correction codes to shift the codec complexity to a joint decoder. Combining MDC with DVC (MDVC) yields a stable codec for mobile encoders. In this paper, to improve the MDVC codec performance, image correlations among the MDVC processing modules were exploited to improve reconstructed video quality and enhance transmission robustness. At the side encoder, a DVC-based adaptive differential pulse code modulation was designed to remove interframe redundancy to enhance rate-distortion performances. For the MDVC central decoding, intradescription and interdescription correlations were utilized to dynamically select the best reconstructed frames from two descriptions, instead of selecting just one description or all key-frames from two descriptions. Experiments showed that, as compared to previous methods, the proposed MDVC control method yielded 1–2 dB higher in image PSNRs for Wyner–Ziv reconstructed frames at the side decoder when encoding low-to-medium complexity videos. For high-complexity videos, it effectively prevents error correction of Wyner–Ziv frames from malfunctioning and yields about 3 dB higher in PSNR. The proposed MDVC central decoder control yields 1–4 dB higher PSNRs, as compared to side decoders. Under lossy transmission, it demonstrates 27–64% smaller PSNR variations, as compared to that of combining key-frames as the decoded video. The proposed MDVC system and control not only improve the DVC reconstructed video quality, but also reduce the quality fluctuation artifacts of MDC coded video for mobile coders. [ABSTRACT FROM PUBLISHER]

To satisfy the requirement of application heterogeneities, the latest H.264/AVC based video coding standard called scalable video coding additional includes temporal, SNR, and spatial scalabilities for frame rate, quality, and frame resolution adaptation. However, these inclusions significantly increase chip design difficulties such as decoding time, memory bandwidth, and area cost. This paper presents an H.264/AVC scalable high profile decoder realization with several optimization techniques to provide high throughput video decoding. For decoding flow, this paper proposes an one-pass macroblock-based quality layer decoding flow for SNR scalability and 71% of external memory bandwidth and 66% of macroblock processing cycles can be saved. For texture padding in interlayer intra prediction, the modified padding flow can save 26% of decoding time. For interlayer predictor design, this paper proposes a centralized concept for accumulation-based calculation of corresponding spatial position, simplified poly-phase interpolator, and efficient motion vector generator to save area cost and decoding time. Furthermore, the residual reconstruction path with the parallel-pipeline architecture is also proposed to cope with the additional decoding complexity and thus leads to 54% of gate count savings compared to the traditional serial-pipeline architecture. Finally, the proposed H.264/AVC scalable high profile decoder design is implemented with 90 nm CMOS technology and it costs 542 k gate count and 39.66 Kbytes on-chip memory while is capable to decode 60 frames/s for CIF+SD480p+HD1080p resolution with three quality layers at 135 MHz operating frequency. [ABSTRACT FROM PUBLISHER]