基于D2D对等协作传输的自适应流媒体方法
Dynamic Adaptive Streaming Based on D2D Cooperative Transportation

作者: 王寿 :清华大学计算机科学与技术系,北京;

关键词: 模板D2DQoE带宽聚合自适应流媒体D2D QoE Bandwidth Aggregation Dash Video Streaming

摘要: 随着移动互联网视频流量的快速增长,移动网络带宽成为限制流媒体传输质量的瓶颈。本文设计了一个多对多的设备到设备(D2D)的协作式自适应流媒体传输框架,将结合蜂窝网络和Wi-Fi自组网络,让设备可以充分利用邻近设备闲置的网络带宽能力来下载和分享视频,并且兼容现有自适应流媒体协议(DASH)。通过集群QoE感知,包括整体的网络带宽、缓存长度以及能耗等反馈信息,来选择合适的视频码率;并通过对等的协作方式,进行资源分配。实验结果表明:该方法相比传统非协作式传输方法,可以有效提高20%的用户满意度。

Abstract: With the recent popularity of mobile device, the video traffic is expanding while the mobile internet access cannot meet the growth of demand for media service. In this paper, we propose a De-vice-to-Device (D2D) cooperative adaptive streaming system, which supports asynchronous downloading and sharing for multi-bitrate videos in the crowd sourced network, to improve the quality of video streaming, and compatible with DASH protocol. We propose a crowd sourced QoE- aware quality adaptation algorithm for DASH and the metrics of QoE consist of network capacity, buffer level and energy. The experimental result shows that the proposed cooperative DASH system can improve 20% of user QOE as compared with the non-cooperative method. 

文章引用: 王寿 (2016) 基于D2D对等协作传输的自适应流媒体方法。 计算机科学与应用, 6, 406-414. doi: 10.12677/CSA.2016.67050

参考文献

[1] Stockhammer, T. (2011) Dynamic Adaptive Streaming over HTTP: Standards and Design Principles. Proceedings of the Second Annual ACM Conference on Multimedia Systems, 133-144.
http://dx.doi.org/10.1145/1943552.1943572

[2] Sodagar, I. (2011) The Mpeg-Dash Standard for Multimedia Streaming over the Internet. IEEE Multimedia, 18, 62-67.
http://dx.doi.org/10.1109/MMUL.2011.71

[3] Oyman, O. and Singh, S. (2012) Quality of Experience for HTTP Adaptive Streaming Services. IEEE Communications Magazine, 50, 20-27.
http://dx.doi.org/10.1109/MCOM.2012.6178830

[4] Balachandran, A., Sekar, V., Akella, A., et al. (2013) Developing a Pre-dictive Model of Quality of Experience for Internet Video. ACM SIGCOMM Computer Communication Review, 43, 339-350.
http://dx.doi.org/10.1145/2534169.2486025

[5] Seufert, M., Egger, S., Slanina, M., et al. (2015) A Survey on Quality of Ex-perience of HTTP Adaptive Streaming. IEEE Communications Surveys & Tutorials, 17, 469-492.
http://dx.doi.org/10.1109/COMST.2014.2360940

[6] Lederer, S., Müller, C. and Timmerer, C. (2012) Dynamic Adaptive Streaming over HTTP Dataset. Proceedings of the 3rd Multimedia Systems Conference, 89-94.
http://dx.doi.org/10.1145/2155555.2155570

[7] Müller, C., Lederer, S. and Timmerer, C. (2012) An Evaluation of Dynamic Adaptive Streaming over HTTP in Vehicular Environments. Proceedings of the 4th Workshop on Mobile Video, 37-42.
http://dx.doi.org/10.1145/2151677.2151686

[8] Yin, X., Sekar, V. and Sinopoli, B. (2014) Toward a Principled Framework to Design Dynamic Adaptive Streaming Algorithms over http. Proceedings of the 13th ACM Workshop on Hot Topics in Networks, 9.
http://dx.doi.org/10.1145/2670518.2673877

[9] Huang, T.Y., Handigol, N., Heller, B., et al. (2012) Confused, Timid, and Unstable: Picking a Video Streaming Rate Is Hard. Proceedings of the 2012 ACM Conference on Internet Measurement Conference, 225-238.
http://dx.doi.org/10.1145/2398776.2398800

[10] Huang, T.Y., Johari, R., McKeown, N., et al. (2015) A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service. ACM SIGCOMM Computer Communication Review, 44, 187-198.
http://dx.doi.org/10.1145/2740070.2626296

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