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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]

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: 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]