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The scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC). It introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2N×2N, inter2N×N, interN×2N, interN×N, inter2N×nU, inter2N×nD, internL×2N and internRx×2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2N×2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%. [ABSTRACT FROM AUTHOR]

Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using scalable video coding (SVC) scheme over highly variable bandwidth conditions, such as cellular networks. SVC’s unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel quality-of-experience metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop layered bin packing (LBP) adaptation algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed, including handling bandwidth prediction errors and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared with the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today’s mobile devices. [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]

An important concept in wireless systems has been QoE-aware video transmission. Such communications are considered not only connection- based communications but also content- aware communications, since the video quality is closely related to the content itself. It becomes necessary, therefore, for video communications to utilize a cross-layer design (also known as joint source and channel coding). To provide efficient methods of allocating network resources, the wireless network uses its cross-layer knowledge to perform UEP solutions. In this article, we summarize the latest video transmission technologies that are based on SVC over MIMO systems with cross-layer designs. To provide insight into video transmission in wireless networks, we investigate UEP solutions in the delivering of video over massive MIMO systems. Our results show that in terms of QoE, SVC layer prioritization, which was considered important in prior work, is not always beneficial in massive MIMO systems; consideration must be given to the content characteristics. [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]

In this paper, we propose a \boldsymbol \lambda -domain rate control algorithm for high efficiency video coding (HEVC) scalable extension. All the commonly used scalabilities including temporal, spatial, and quality scalability are taken into consideration. The proposed algorithm mainly has three key contributions. First, we propose an optimal initial target bits and initial encoding parameters determination algorithm for the first frame of each layer to achieve the best rate-distortion (R-D) performance. Second, an optimal bit allocation algorithm taking both the intra and inter layer dependence into consideration is proposed for the inter frames under spatial and quality scalability cases. Third, since the coding scheme of HEVC scalable extension with multiple layers is even more flexible than HEVC, an adaptive updating algorithm for R-\boldsymbol \lambda model is proposed to control the bits per frame even more precisely. The experimental results demonstrate that the proposed \boldsymbol \lambda -domain rate control algorithm can bring both more precise bitrate accuracy and better R-D performance compared with the previous rate control algorithms for HEVC scalable extension. [ABSTRACT FROM PUBLISHER]

The scalable extension of HEVC (known as SHVC), the recent scalable video coding standard, results in an improved compression performance at the cost of significant increase in computational coding complexity. One of the main factors that contribute to the SHVC encoder complexity is choosing the best partitioning structure for the coding tree units (CTUs). Our study focuses on developing a scheme for predicting the CTU structure in the quality and spatial scalable extension of HEVC. The proposed scheme uses the CTU partitioning structure of the already encoded CTUs in the enhancement layers (ELs) and base layer (BL) to predict the coding unit sizes of the to-be-encoded CTUs in the EL. Performance evaluations confirm that our proposed complexity reduction scheme significantly reduces the execution time of the SHVC encoder, while maintaining the overall quality of the coded streams. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we propose a novel joint source-channel coding (JSCC) scheme for scalable video transmission over multiple-input multiple-output (MIMO) systems. By exploiting the diversity of MIMO antennas and forward error correction (FEC)-based protection, our method aims to provide unequal error protection (UEP) for the video layers, which are mapped to appropriate antennas. Moreover, JSCC is also considered that we extract a proper subset of video layers and allocate suitable FEC redundancy to them. Jointly considering video layer extraction, FEC rate allocation, and video layer scheduling, we are able to achieve UEP so as to minimize end-to-end distortion. We formulate the scheme as a nonlinear integer optimization problem, which is known to be NP-hard. To find a near-optimal solution efficiently, we propose a low-complexity branch-and-bound algorithm, which partitions the original problem into a series of subproblems by a video layer branching technique. In each branch, the upper and lower distortion bounds are derived. In particular, we transform the video layer scheduling subproblem into a 0/1 multiple knapsack problem, which is NP-complete, and employ an evolutionary Lagrangian method to find a solution efficiently. For the FEC allocation subproblem, a Lagrange duality algorithm with fuzzy surrogate subgradient is proposed. The experimental results demonstrate that the proposed method has good efficiency while achieving close performance to the optimal results. [ABSTRACT FROM PUBLISHER]

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]

Recently, Dynamic Adaptive Streaming over HTTP (DASH) has attracted significant attention. In this paper, we consider DASH-based transmission of scalable videos in wireless broadband access networks (e.g., long-term evolution and WiMAX), and propose three methods to enhance the quality of experience of wireless DASH users. First, we design an improved mapping scheme from scalable video coding layers to DASH layers that can provide the desired bitrates, enhance the video end-to-end throughput, and reduce the HTTP communication overhead. Second, we develop a DASH-friendly scheduling and resource allocation algorithm by integrating the DASH-based media delivery and the radio-level adaptation via a cross-layer approach. It utilizes the characteristics of video content and scalable video coding, and greatly reduces the possibility of video playback interruption by considering the client buffer status. The optimization problem is formulated as a mixed binary integer programming problem, and is solved by a subgradient method. Finally, a DASH proxy-based bitrate stabilization algorithm is proposed to improve the video playback smoothness that can achieve the desired tradeoff between playback quality and stability. Simulations with the Qualnet tool demonstrate that our schemes achieve better performances than other methods in the literature. [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]

Hierarchical B-frame coding was introduced into H.264/SVC to provide temporal scalability and improve coding performance. A content analysis-based adaptive group of picture structure (AGS) can further improve the coding efficiency, but damages the inter-frame correlation and temporal scalability of hierarchical B-frame to different degrees. In this paper, we propose a group of pictures (GOP) adaptation coding method based on the positions of video cuts. First, the cut positions are accurately detected by the combination of motion coherence (MC) and mutual information (MI); then the GOP is adaptively and proportionately set by the analysis of MC in one scene. In addition, we propose a binary tree algorithm to achieve the temporal scalability of any size of GOP. The results for test sequences and real videos show that the proposed method reduces the bit rate by up to about 15%, achieves a performance gain of about 0.28-1.67 dB over a fixed GOP, and has the advantages of better transmission resilience and video summaries. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This paper presents a new Scalable Video Codec (SVC)-based hybrid adaptive video streaming scheme, named HAVS, for mobile devices in wireless environments. The proposed approach takes two existing video streaming technologies, viz., progressive download and adaptive streaming, and switches them in a hybrid manner. To this end, HAVS employs the H.264/SVC encoding scheme, where each video chunk is encoded into one base layer and several enhancement layers. Since clients request the base layer every time a video is streamed, HAVS performs progressive download for the base layer and adaptive streaming for the enhancement layers. Through wireless test-bed experiments, it is demonstrated that the proposed scheme can be easily implemented on mobile devices without any server-side modification. This scheme effectively prevents video freeze thereby providing better quality video streaming than the existing non-hybrid streaming technologies. [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]

In this letter, a new model for generating SVC-like traffic and estimating its quality is introduced. Comprising two independent components — i.e., the traffic generator and the quality estimator — the model can significantly speed up the development of SVC applications in various use-cases by relying on approximated properties of streams instead of real traces. The model is verified based on a set of video streams and the performance of its components is assessed. [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]

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]

To satisfy the video application diversities, an extension of H.264/advanced video coding (AVC), called scalable video coding (SVC), is designed to provide multiple demanded video data via a single video encoder. However, constructed on the fundamental of H.264/AVC, the complexity of SVC is much higher than that of H.264/AVC. In this paper, a VLSI design for all-intra scalable video encoder is proposed to aim at efficient scalable video encoding. First, the memory bandwidth requirements for several encoding methods are analyzed to find out the best encoding method which can achieve best tradeoff between internal memory usage and external memory access. Afterward, an all-intra SVC encoder combined with several advanced techniques, including fast intra prediction algorithm, efficient syntax element encoding approach in context-adaptive variable-length coding, and hardware-efficient techniques, are implemented in a macroblock (MB)-level pipeline to increase data throughput. Implementation results demonstrate that our proposed SVC encoder can process more than 594-k MBs per second, which is equivalent to the summation of 60 high-definition, 1080-p, SD 480-p, and common intermediate format frames under 135-MHz working frequency. The proposed design consumes 258-K gate counts when synthesized by 90-nm CMOS technology. [ABSTRACT FROM PUBLISHER]

We consider the problem of scalable video streaming from a server to multinetwork clients over heterogeneous access networks, with the goal of minimizing the distortion of the received videos. This problem has numerous applications including: 1) mobile devices connecting to multiple licensed and ISM bands, and 2) cognitive multiradio devices employing spectrum bonding. In this paper, we ascertain how to optimally determine which video packets to transmit over each access network. We present models to capture the network conditions and video characteristics and develop an integer program for deterministic packet scheduling. Solving the integer program exactly is typically not computationally tractable, so we develop heuristic algorithms for deterministic packet scheduling, as well as convex optimization problems for randomized packet scheduling. We carry out a thorough study of the tradeoff between performance and computational complexity and propose a convex programming-based algorithm that yields good performance while being suitable for real-time applications. We conduct extensive trace-driven simulations to evaluate the proposed algorithms using real network conditions and scalable video streams. The simulation results show that the proposed convex programming-based algorithm: 1) outperforms the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about 10–15 dB higher video quality; 2) reduces average delivery delay by over 90% compared to DCCP; 3) results in higher average video quality of 4.47 and 1.92 dB than the two developed heuristics; 4) runs efficiently, up to six times faster than the best-performing heuristic; and 5) does indeed provide service differentiation among users. [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]

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]

In this paper, we investigate optimal resource allocation and scheduling for scalable video multicast over wireless networks. The wireless video multicasting is a best-effort service which has limited transmission energy and channel access time. To cater for multi-resolution videos to heterogeneous clients and for channel adaptation, we adopt scalable video coding (SVC) with spatial, temporal and quality scalabilities. Our scalable video multicast system consists of a channel probing stage to gather the channel state information and a transmission stage to multicast videos to clients. We formulate the optimal resource allocation problem by maximizing the video quality of the clients subject to transmission energy and channel access constraints. We show that the problem is a joint optimization of the selection of modulation and coding scheme (MCS), and the transmission power allocation. By imposing a quality-of-service (QoS) constraint on the packet loss rate, we simplify the original problem to a binary knapsack problem which can be solved by a dynamic programming approach. Specifically, we first propose a multicast scheduling scheme based on the quality impact of each SVC layer. Guided by the content-aware multicast scheduling, we optimize the resource allocation for each SVC layer sequentially. Solution at each step takes into account of the channel condition, remaining resources, and client requirements. The proposed scheme is of linear complexity and leads to the maximized video quality for the admitted clients, while satisfying the energy budget and channel access constraints. Experiment results demonstrate that our scheme achieves notable video quality improvements for multicast clients, when compared to the state-of-the-art video multicast method. [ABSTRACT FROM PUBLISHER]

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]

Fast mode decision algorithms have been widely used in the video encoder implementation to reduce encoding complexity yet without much sacrifice in the coding performance. Optimal stopping theory, which addresses early termination for a generic class of decision problems, is adopted in this paper to achieve fast mode decision for the H.264/Scalable Video Coding standard. A constrained model is developed with optimal stopping, and the solutions to this model are employed to initialize the candidate mode list and predict the early termination. Comprehensive simulation results are conducted to demonstrate that the proposed method strikes a good balance between low encoding complexity and high coding efficiency. [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]

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]

A novel spatial-layer rate-control algorithm is proposed for scalable video coding (SVC). First, by analyzing the relationship among the best initial quantization parameter (Qp), channel bandwidth, and the initial frames' complexity measure, an adaptive Qp-initialization model is introduced to determine the starting Qp value for not only the base layer but also the spatial enhancement layers of SVC. Then, a two-stage Qp -determination scheme is designed to improve the rate-control performance for the spatial-layer SVC with an efficient frame-complexity prediction method and an adaptive model-parameter update technique employed. Experimental results demonstrate the effectiveness of the proposed Qp -initialization scheme and the two-stage Qp-determination algorithm. By comparison with two other benchmark rate-control algorithms, the proposed rate-control algorithm is able to control constrained bit rates accurately with better rate-distortion performance. [ABSTRACT FROM PUBLISHER]

Selective protection can exploit dependency coding properties to effectively perform partial protection on scalable video coding (SVC) bitstreams since protecting frames in lower scalability layers affects visual quality of the reconstructed frames in higher scalability layers. In this paper, we propose a selective protection scheme that maximizes protection effects by the minimum number of encoded frames to be protected in the SVC bitstream domain. We first model the SVC dependency coding structure as a directed acyclic graph which is characterized with an estimated visual quality value as the attribute at each node. In addition, a visual quality estimation model is proposed based on the proportions of intra-predicted and inter-predicted MBs, amounts of residues, and estimated visual quality of reference frames. The proposed selective protection scheme traverses the dependency graph to find optimal protection paths that can give the maximum visual quality degradation. Experimental results show that, compared to the existing protection schemes, the proposed selective protection scheme reduces computation complexity in the number of protected frames, the amount of protected data, and the protection time saving. The SVC file format specification supports the carriage of selectively protected bitstreams based on the concept of our selective protection in dependency coding structure of SVC. [ABSTRACT FROM AUTHOR]

We propose a novel visual scalable video coding (VSVC) framework, named VSVC H.264/AVC. In this approach, the non-uniform sampling characteristic of the human eye is used to modify scalable video coding (SVC) H.264/AVC. We exploit the visibility of video content and the scalability of the video codec to achieve optimal subjective visual quality given limited system resources. To achieve the largest coding gain with controlled perceptual quality degradation, a perceptual weighting scheme is deployed wherein the compressed video is weighted as a function of visual saliency and of the non-uniform distribution of retinal photoreceptors. We develop a resource allocation algorithm emphasizing both efficiency and fairness by controlling the size of the salient region in each quality layer. Efficiency is emphasized on the low quality layer of the SVC. The bits saved by eliminating perceptual redundancy in regions of low interest are allocated to lower block-level distortions in salient regions. Fairness is enforced on the higher quality layers by enlarging the size of the salient regions. The simulation results show that the proposed VSVC framework significantly improves the subjective visual quality of compressed videos. [ABSTRACT FROM AUTHOR]

A novel frame-level rate control (RC) algorithm is presented in this paper for temporal scalability of scalable video coding. First, by introducing a linear quality dependency model, the quality dependency between a coding frame and its references is investigated for the hierarchical B-picture prediction structure. Second, linear rate-quantization (R-Q) and distortion-quantization (D-Q) models are introduced based on different characteristics of temporal layers. Third, according to the proposed quality dependency model and R-Q and D-Q models for each temporal layer, adaptive weighting factors are derived to allocate bits efficiently among temporal layers. Experimental results on not only traditional quarter common intermediate format/common intermediate format but also standard definition and high definition sequences demonstrate that the proposed algorithm achieves excellent coding efficiency as compared to other benchmark RC schemes. [ABSTRACT FROM AUTHOR]

Multimedia data delivered to mobile devices over wireless channels or the Internet are complicated by bandwidth fluctuation and the variety of mobile devices. Scalable video coding has been developed as an extension of H.264/AVC to solve this problem. Since scalable video codec provides various scalabilities to adapt the bitstream for the channel conditions and terminal types, scalable codec is one of the useful codecs for wired or wireless multimedia communication systems, such as IPTV and streaming services. In such scalable multimedia communication systems, video quality fluctuation degrades the visual perception significantly. It is important to efficiently use the target bits in order to maintain a consistent video quality or achieve a small distortion variation throughout the whole video sequence. The scheme proposed in this paper provides a useful function to control video quality in applications supporting scalability, whereas conventional schemes have been proposed to control video quality in the H.264 and MPEG-4 systems. The proposed algorithm decides the quantization parameter of the enhancement layer to maintain a consistent video quality throughout the entire sequence. The video quality of the enhancement layer is controlled based on a closed-form formula which utilizes the residual data and quantization error of the base layer. The simulation results show that the proposed algorithm controls the frame quality of the enhancement layer in a simple operation, where the parameter decision algorithm is applied to each frame. [ABSTRACT FROM PUBLISHER]