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Dynamic Adaptive Streaming over Http (DASH) -based video streaming applications are becoming increasingly prevalent over the mobile Internet. Many efforts have been made to optimize their performances. Multipath video streaming that simultaneously utilizes multiple wireless networks for video content delivery is a common method. Another effective approach is the cross-layer video streaming optimization that jointly takes the parameters at different protocol layers into account. However, multipath streaming schemes mainly focus on how to efficiently utilize multiple wireless networks and the collaboration of parameters at different layers in each network is neglected. Likewise, the cross-layer schemes normally optimize the parameters at different layers in purely one network without fully utilizing the aggregated bandwidths of multiple available wireless networks. Therefore, both of them are sub-optimal and might suffer from degrading performance. In this paper, we propose a joint Cross-layer DASH-based multipath video streaming scheme that takes advantage of bandwidth aggregation of multiple wireless networks and further improves the performance by optimizing the different layers' parameters in each network with a cross-layer manner. In the proposed scheme, the LTE and 802.11ac networks are adopted. The bitrate of DASH-based video chunk at application layer, the rate allocation among networks and the Modulation and Coding Scheme (MCS) at physical layers in LTE and 802.11ac downlink are jointly optimized. We also compare our proposed scheme to state-of-the-art schemes using trace-driven experiments. Experimental results show that our proposed scheme outperforms state-of-the-art schemes in terms of PSNR, normalized QoE, and balance between video bitrate and rebuffering penalty. [ABSTRACT FROM AUTHOR]

3D video for tele-medicine applications is gradually gaining momentum since the 3D technology can provide precise location information. However, the weak link for 3D video streaming is the necessary wireless link of the communication system. Neglecting the wireless impairments can severely degrade the performance of 3D video streaming that communicates complex critical medical data. In this paper, we propose systematic methodology for ensuring high performance of the 3D medical video streaming system. First, we present a recursive end-to-end distortion estimation approach for MVC (multiview video coding)-based 3D video streaming over error-prone networks by considering the 3D inter-view prediction. Then, based on the previous model, we develop a cross-layer optimization scheme that considers the LTE wireless physical layer (PHY). In this optimization, the authentication requirements of 3D medical video are also taken into account. The proposed cross-layer optimization approach jointly controls and manages the authentication, video coding quantization of 3D video, and the modulation and channel coding scheme (MCS) of the LTE wireless PHY to minimize the end-to-end video distortion. Experimental results show that the proposed approach can provide superior 3D medical video streaming performance in terms of peak signal-to-noise ratio (PSNR) when compared to state-of-the-art approaches that include joint source-channel optimized streaming with multi-path hash-chaining based-authentication, and also conventional video streaming with single path hash-chaining-based authentication. [ABSTRACT FROM AUTHOR]

The traditional Sum of Squared Error (SSE)-based cross-layer optimization has been shown to be an effective way to improve the quality of service for wireless video streaming. However, recent research works show that the SSE-based optimization metric does not always provide the video distortion measurement that matches well with the video quality degradation observed by the human vision system. Taking advantage of the Structural SIMilarity (SSIM) metric in measuring the video perceptual quality, a SSIM-based error-resilient cross-layer optimization scheme is proposed to improve the perceptual quality for the real-time wireless video streaming in this paper. Besides the video data rate adjustment and the link adaption including the Modulation and Coding Scheme (MCS) selection, the error-resilient Rate-Distortion Optimization (RDO) for each encoding unit is introduced into the cross-layer optimization process to ensure that the video data are transmitted efficiently and reliably over the time-varying wireless channel. In addition, to ensure that cross-layer optimization scheme is more practical, a low-complexity optimal parameter selection algorithm that exploits the MCS-SNR relationship and the Rate-Quantization (R-Q) model is proposed. Experimental results show that significant performance improvements in terms of the perceptual video quality and the computational complexity are achieved for the proposed cross-layer optimization scheme. [ABSTRACT FROM AUTHOR]

3D video distribution over P2P networks has been thought as a promising way for 3D video entering home. The convergence of scalable 3D video coding and P2P streaming can provide diverse 3D experiences for heterogeneous clients with high distribution efficiencies. However, the conventional chunk segmentation and scheduling algorithms originally aiming at the non-scalable 2D video streaming are not very efficient for scalable 3D video streaming over P2P networks due to the particular data characteristics of scalable 3D video. Based on this motivation, this paper first presents a playback length changeable 3D video chunk segmentation (PLC3DCS) algorithm to provide different error resilience strengths to video and depth as well as layers with different importance levels in the 3D video transmission. Then, a hybrid-priority based chunk scheduling (HPS) algorithm is proposed to be tied in with the proposed chunk segmentation algorithm to further promote the overall 3D video P2P streaming performance. The simulation results show that the proposed PLC3DCS algorithm with the corresponding HPS can increase the success delivery rates of chunks with more important levels, and further improve the user’s quality of 3D experience. [ABSTRACT FROM AUTHOR]

Highlights: [•] A 3D experience oriented 3D video cross-layer optimization method is proposed. [•] Networking-related 3D visual experience model for 3D video streaming is presented. [•] 3D video characteristics are fully considered in the cross-layer optimization. [•] MAC layer channel allocation and physical layer MCS are systematically optimized. [•] Results show that our method obtains superior 3D visual experience to others. [Copyright &y& Elsevier]

Abstract: The SSIM-based rate-distortion optimization (RDO) has been verified to be an effective tool for H.264/AVC to promote the perceptual video coding performance. However, the current SSIM-based RDO is not efficient for improving the perceptual quality of the video streaming application over the error-prone network, because it does not consider the transmission induced distortion in the encoding process. In this paper, a SSIM-based error-resilient RDO scheme for H.264/AVC is proposed to improve the wireless video streaming performance. Firstly, with the help of the SSE-based RDO, we present a low-complexity Lagrange multiplier decision method for the SSIM-based RDO video coding in the error-free environment. Then, the SSIM-based decoding distortion of the user end is estimated at the encoder and is correspondingly introduced into the RDO to involve the transmission induced distortion into the encoding process. Further, the Lagrange multiplier is theoretically derived to optimize the encoding mode selection in the error-resilient RDO process. Experimental results show that the proposed SSIM-based error-resilient RDO can obtain superior perceptual video quality (more structural information) to the traditional SSE-based error-resilient RDO for wireless video streaming at the same bit rate condition. [Copyright &y& Elsevier]

Abstract: This paper presents a rate-distortion (RD) optimized interactive streaming method for multiview video pre-compressed by H.264 Joint Multiview Video Model (JMVM). In the proposed method, multiple encodings are first used to facilitate the flexible server–client interaction. Second, a RD-optimized scheduling strategy is provided to guarantee the optimal view-dependent delivery of multiview video. In the RD-optimized scheduling strategy, a distortion model is proposed to estimate the expected end-to-end distortion by accounting for both coding and packet-loss-induced distortions, as well as rendering-induced distortion. With the end-to-end distortion model, the server can select the optimal encoding combination for transmission. Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding. In addition, it has better error-resilience performance to combat with packet-losses over the Internet protocol (IP) networks. [Copyright &y& Elsevier]

Currently, VR video delivery over 5G systems is still a very complicated endeavor. One of the major challenges for VR video streaming is the expectations for low latency that current mobile networks can hardly meet. Network caching can reduce the content delivery latency efficiently. However, current caching schemes cannot obtain ideal results for VR video since it requests the viewport interactively. In this paper, we propose a tiled scalable VR video caching scheme over 5G networks. VR chunks are first encoded into multi-granularity quality layers, and are then partitioned into tiles to facilitate viewport data access. By accommodating the 5G network infrastructure, the tiles are cooperatively cached in a three-level hierarchal system to reduce delivery latency. Furthermore, a quality-adaptive request routing algorithm is designed to cater for the 5G bandwidth dynamics. Experimental results show that the proposed scheme can reduce the transmission latency over conventional constant bitrate video caching schemes. [ABSTRACT FROM AUTHOR]

The small-cell wireless network (SCN) paradigm is based on the deployment of low-power small cells close to the user so that spatial re-use is increased. Small cell base stations (SCBSs) can be connected either with a wired or a wireless backhaul link to the core network. However, since these SCBSs are expected to be deployed in significant numbers [1] , the increased density creates not only new problems but also optimization opportunities. In this paper we work towards the full exploitation of the broadcast advantage that dense SCNs offer in order to improve the video quality of unicast streaming applications. To accomplish our goal we design a protocol that exploits the broadcast channel in the neighborhood of each small cell by using the SCBSs opportunistically as a relay stations. Each SCBS can overhear packet transmissions both from the macro BS (MBS) and also from other SCBSs in its neighborhood. Furthermore, since a packet may be available at multiple SCBSs, we propose an algorithm for optimized relay selection that takes into account the content of each specific video packet. This algorithm selects jointly the optimal small cell relay and video packet for forwarding. Both the overhearing protocol and the relay selection algorithm use only passively collected information and require no explicit message passing. The evaluation of the two proposed sub-systems shows significant performance improvements under different key system configurations for the video content, the number of neighboring SCBS, and the type of the backhaul connection. [ABSTRACT FROM AUTHOR]