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Non-orthogonal multiple access (NOMA) is promising to enable growing video services with requirements of massive traffic and low latency. In this paper, we investigate a novel multi-user NOMA system design for video delivery. Different from data delivery considered in existing NOMA systems, video distortion is taken into consideration for the design and optimization of the proposed scheme. We first propose a cross-layer scalable video delivery scheme over NOMA wireless networks based on given user grouping. By adopting a semi-analytical rate distortion model for encoded video sequences and investigating the physical layer model for the multi-carrier NOMA network, the distortion-aware power allocation problem is formulated with the goal to minimize the system end-to-end distortion with quality-of-service requirements. To solve such a non-convex problem, we design an efficient algorithm by exploiting its hidden monotonic property to approximate the optimal solution. Inspired by the decoding order in NOMA, a fast algorithm is developed by combining water-filling and greedy strategies to further tame the computational complexity. Simulations results have verified the advantages of the proposed cross-layer scheme with distortion-aware power allocation algorithms, against two existing NOMA schemes and an OMA scheme. [ABSTRACT FROM AUTHOR]

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems. [ABSTRACT FROM AUTHOR]

Mobile display has become one of the most power-hungry components in mobile video viewing. Currently, mobile devices can reduce the display energy by performing dynamic brightness scaling (DBS) under the distortion constraint of video signals. We observe that there is a pitfall preventing current practice from systematic display energy reduction. In particular, existing objective DBS schemes lack direct connection to the subjective human perception on DBS-enabled videos, which is the key to achieving human-centered energy-experience optimization. To overcome this pitfall, we present CrowdDBS, a crowdsourced display energy reduction framework for mobile video viewing. CrowdDBS is empowered by a set of crowdsourcing studies that uncover the relationship between human perception and DBS frequency, magnitude, and temporal consistency, respectively. Motivated by the insights obtained from these studies, CrowdDBS employs a suit of designs and a DBS optimization framework to optimize the energy-experience tradeoff in mobile video viewing. Comprehensive experimental results and user evaluations under a variety of practical settings show that CrowdDBS can achieve 37 percent device energy reduction on average while guaranteeing satisfactory user experience in mobile video viewing. [ABSTRACT FROM AUTHOR]

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a \lambda -domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the \lambda -domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R- \lambda model developed in our initial work, a D- \lambda model is properly constructed to complete a comprehensive framework of \lambda -domain R-D analysis. Based on this comprehensive \lambda -domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software. [ABSTRACT FROM PUBLISHER]

This paper presents a network slice design for ultra high definition (UHD) video broadcast/multicast to achieve higher network efficiency and improved quality of experience (QoE). The proposed network slice design consists of a rateless source compression scheme and an analog-coded SoftCast scheme. The rateless Spinal code is adopted to compress the video source at content server and the compressed source is transmitted from content server across wireless core network to the base station. An a priori information-assisted Spinal decoder is designed to utilize the sparsity of bit planes for compression. In the analog-coded SoftCast scheme, we design a new chaotic function-based analog code with negligible power penalty for the generalized Gaussian-distributed source in SoftCast because the existing chaotic functions designed for uniformly distributed sources suffer from serious power penalty in SoftCast. We also design a maximum a posteriori probability decoding algorithm for the proposed analog code in order to exploit the statistics of video source as a priori information to improve the performance. The experimental results show that the proposed rateless code-based compression scheme achieves efficient compression and approaches the bound of binary erasure channel. In particular, the 1/2 analog-coded SoftCast has almost 2 dB gain over conventional SoftCast with two repetitions, and the 1/3 analog-coded SoftCast has almost 3 dB gain over conventional SoftCast with three repetitions. The system simulations for the broadcast system show higher network capacity and improved QoE in the proposed UHD slice, because the reconstructed video quality of each user is commensurate with its channel condition. [ABSTRACT FROM PUBLISHER]

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies. [ABSTRACT FROM PUBLISHER]

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: 1) the performance limitations of wireless networks in bandwidth and reliability; 2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and 3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source–forward error correction (FEC) coding framework dubbed adaptive source–FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source–FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source–FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate. [ABSTRACT FROM PUBLISHER]

Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible prediction structures is unlimited, and it is also observed that the coding bit-rate can be reduced by increasing the number of bi-directionally encoded views in the prediction structure. However, none work has been conducted to analyze the relationship of the prediction structure with its coding performance. In light of this observation, we first design a new LF-MVC prediction structure by extending the inter-view prediction into a two-directional parallel structure. Analytical models for source coding rate and encoding time are developed to analyze their relationships with the prediction structure, and are proven to be well-matched to our experimental results. Experimental evaluation of two LF video sequences demonstrates that the proposed LF-MVC prediction structure can achieve a factor of 26% bit-rate reduction against the conventional MVC prediction structure for an LF video with $5\times 5$ views, and a further 34% bit-rate reduction for an LF video with a larger $10\times 10$ views. Compared with the state-of-the-art MVC-based LF video coding prediction structures in the literature, LF-MVC can achieve the best coding performance, and with its high encoding efficiency, is well suited for deployment in practical LF-based 3D systems. [ABSTRACT FROM PUBLISHER]

High-frame-rate (HFR) video technology is becoming widely implemented in popular multimedia applications (e.g., Youtube and cloud gaming) to provide a smooth viewing experience, while transmission control protocol (TCP) is pervasively adopted as the transport-layer solution for video communications to achieve firewall traversal and network friendliness. However, it is severely challenging to effectively deliver real-time HFR video over TCP: 1) HFR video streaming features high transmission rate, enhanced frame density, and stringent delay constraint and 2) the packet retransmission and congestion control mechanisms in TCP may cause frequent throughput fluctuations and deadline violations. Motivated by addressing these critical issues, this research presents an application-layer forward error correction (FEC) framework dubbed Raptor coded HFR video over TCP (ROCHET). First, we develop a mathematical model to analyze the frame-level distortion of systematic Raptor code-based HFR video communication over TCP in wireless networks. Second, we propose a joint approximate distortion estimation and Raptor coding adaption solution to minimize the sum of total distortion. The proposed ROCHET is able to effectively leverage unequal error protection and TCP state analysis to enhance streaming video quality. We conduct the performance evaluation through extensive emulations in Exata involving real-time HFR video encoded with H.264 codec. Compared with the existing FEC coding schemes, ROCHET achieves appreciable improvements in terms of video peak signal-to-noise ratio, goodput, and frame success rate. Thus, ROCHET is recommended for TCP-based HFR video transmission over wireless networks. [ABSTRACT FROM PUBLISHER]

We propose an adaptive hybrid digital–analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital–analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios. [ABSTRACT FROM PUBLISHER]

High frame rate (HFR) video is emerging as a new paradigm in popular multimedia applications (e.g., cloud gaming) to achieve smooth viewing experience perceived by end-users. In the context of HFR streaming video, end-to-end distortion and sending frame rate are equally important to the perceptual quality. This study presents a modeling-based approach to optimizing the HFR video transmission over wireless networks. First, we develop an analytical model dubbed FRIED (Frame Rate versus vIdEo Distortion) to characterize the tradeoff between sending frame rate and end-to-end video distortion. Second, we propose a Joint frAme Selection and FEC (Forward Error Correction) cOding (JASCO) approach based on the FRIED model to optimize the transmission performance. The efficacy of the proposed JASCO is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that JASCO outperforms the reference approaches in improving video peak signal-to-noise ratio (PSNR) at the same frame rate. Or conversely, JASCO is able to achieve higher received frame rate while guaranteeing the same video PSNR. [ABSTRACT FROM PUBLISHER]

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme’s capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel smart downlink scheduling scheme to enhance the performance of multimedia transmission over long-term evolution (LTE) networks. LTE represents a promising framework for the next-generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has largely been designed for general data traffic without adequate consideration of multimedia characteristics. Moreover, the hard handoff (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot deliver the expected quality of service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this paper a QoS-driven smart downlink scheduling scheme for enhanced multimedia transmission over LTE networks. The proposed design shall take the following QoS metrics into consideration: 1) the delay constraint of voice-over-Internet protocol flow; 2) the packet deadline of video flow; and 3) the service degradation induced by the hard HO procedure. We achieve the design objectives by creating three QoS-driven operational control modules: 1) a transmission delay control module to ensure the on-time arrival of various types of multimedia data; 2) an HO control module to warrant continuous multimedia services when the user moves across a cell boundary; and 3) a resource allocation module to strategically map the requested flows to best fit radio resource blocks. The simulation results confirm the performance gain of the proposed scheme. [ABSTRACT FROM AUTHOR]

It is now a common knowledge for mobile users to notice that the video viewing experience for the same device and same content can be dramatically different. The main reason for such difference is the dynamic viewing environment of mobile device which may change from indoor to outdoor or from walking to riding in a traveling vehicle. In this paper, we report our recent study on how mobile video perception is different from conventional TV viewing experiences in the living room and on which adaption strategy we can take to generate video bitstreams that matches better with the dynamic viewing environment. We first identify several key factors that would impact the mobile video perception, including display size and viewing distance, surrounding luminance, and body movement. These factors will be evaluated by context-aware subjective tests specifically designed to quantify the correlations between these factors and the viewing experience. Based on such evaluation, we develop a mobile video quality assessment tool to measure the just noticeable distortion in a given viewing environment. With appropriate rate adaptation to viewing context parameters, we show that an environment-aware video bit-stream preparation is able to enhance mobile video experience by exploiting inherent video signal redundancy under a given viewing context. Such adaptation can also reduce the bitrate requirement by an average of 30%. [ABSTRACT FROM PUBLISHER]

Video adaptation through transcoding can provide both bit-rate reduction and resolution reduction to meet various requirements from display devices to network links. One important issue in video transcoding is the design of rate control algorithm in order to achieve target bit rate by adjusting certain coding parameters. Among them, proper selection of initial quantization parameter (QP) has been shown to induce noticeable impact on the performance of video transcoding scheme. Current approaches in initialQP determination are either too complicated or lacking adequate accuracy. This paper presents an adaptive QP initialization for H.264/AVC transcoding. First, we carefully build the models of R-MSE and QP-PSNR. Then, we introduce an R-QP model and allocate an optimal target buffer to the first frame by considering its temporal importance. The analysis and the R-QP model lead to a novel scheme to determine the initial QP adaptively to achieve more accurate estimation. Experiments have been carried out to demonstrate that substantial gains in objective quality measures can be consistently obtained. Without increasing complexity in transcoding system, the proposed adaptive initial QP scheme outperforms current existing schemes for various video sequences tested in this research. [ABSTRACT FROM PUBLISHER]

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users? perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure. [ABSTRACT FROM AUTHOR]

The 11 papers in this special issue cover a range of topics and can be logically organized in three groups, focusing on QoE-aware media protection, QoE assessment and modelling, and multi-user-QoE management. [ABSTRACT FROM AUTHOR]