Online Cloud Transcoding and Distribution for Crowdsourced Live Game Video Streaming
Online Cloud Transcoding and Distribution for Crowdsourced Live Game Video Streaming
复制标题
众包直播游戏视频流的在线云转码和分发
DOI：
10.1109/tcsvt.2016.2556584
复制
发表时间：
2017-08
影响因子：
8.4
通讯作者：
Guoqing Zhang
中科院分区：
文献类型：
--
作者：
Yuanhuan Zheng;Di Wu;Yihao Ke;Can Yang;Min Chen;Guoqing Zhang
近年来，在富媒体生成设备和便捷的互联网接入的赋能下，众包游戏视频直播(CLGVS)已经成为最受欢迎的互联网服务之一。Twitch.tv是世界上最知名的CLGVS平台，允许游戏玩家通过互联网播放他们的游戏视频。随着移动设备的普及，观众可以随时随地在任何设备(例如智能手机、平板电脑或个人电脑)上观看游戏玩家玩视频游戏。然而，用户设备的异构性使得传统的解决方案很难确保用户感知的质量。在本文中，我们从CLGVS服务提供商的角度来解决性价比高的自适应游戏视频直播问题。我们的目标是通过动态做出实时转码决策、比特率适配决策和数据中心分配决策，最大限度地降低CLGVS服务提供商的运营成本。同时，我们的算法也保证了观众足够好的服务质量。由于游戏流派的多样性，我们在设计算法时也考虑了游戏流派。为了达到上述目的，我们将问题转化为一个约束随机优化问题。利用Lyapunov优化框架，我们得到了具有可证明性能界的在线策略。为了评估我们提出的算法的有效性，我们进一步进行了一系列轨迹驱动的仿真。实验结果证明了该算法在运行成本和服务质量方面的有效性。与其他算法相比，我们提出的算法可以在获得足够好的观众QOE的同时，将运营成本降低高达50%。
In recent years, empowered by rich media generation devices and convenient Internet access, Crowdsourced Live Game Video Streaming (CLGVS) has become one of the most popular Internet services. Twitch.tv, the most well-known CLGVS platform in the world, allows gamers to broadcast their gaming videos over the Internet. With the prevalence of mobile devices, viewers can watch gamers playing video games anywhere, anytime, on any devices (e.g., smartphones, tablets, or personal computers). However, the heterogeneity of user devices makes conventional solutions hard to ensure user-perceived quality. In this paper, we address the problem of cost-effective adaptive live game video streaming from the perspective of CLGVS service providers. Our purpose is to minimize the operational cost for CLGVS service providers by making live transcoding decisions, bit-rate adaptation decisions, and datacenter assignment decisions dynamically. Meanwhile, our algorithm also ensures good-enough service quality for viewers. Due to the diversity of game genres, we also consider game genres when designing our algorithm. To achieve the above purpose, we formulate the problem into a constrained stochastic optimization problem. By leveraging the Lyapunov optimization framework, we derive the online strategy with provable performance bound. To evaluate the effectiveness of our proposed algorithm, we further conduct a series of trace-driven simulations. The experimental results demonstrate the effectiveness of our algorithm in terms of operational cost and service quality. Our proposed algorithm can reduce operational cost by up to 50% while achieving good-enough viewer QoE compared with other alternatives.
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DOI：
10.1145/2465958.2465971
发表时间：
2013-06
期刊：
--
影响因子：
--
作者：
Thomas Smith;Marianna Obrist;Peter C. Wright
通讯作者：
Thomas Smith;Marianna Obrist;Peter C. Wright
影响因子：
7.3
作者：
Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;A. Khisti
通讯作者：
Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;A. Khisti
DOI：
10.1145/2537855
发表时间：
2014-01-01
影响因子：
5.1
作者：
Huang, Chun-Ying;Chen, Kuan-Ta;Hsu, Cheng-Hsin
通讯作者：
Hsu, Cheng-Hsin
DOI：
10.1145/2072298.2071991
发表时间：
2011-11
期刊：
Proceedings of the 19th ACM international conference on Multimedia
影响因子：
--
作者：
Kuan-Ta Chen;Yu-Chun Chang;P. Tseng;Chun-Ying Huang;C. Lei
通讯作者：
Kuan-Ta Chen;Yu-Chun Chang;P. Tseng;Chun-Ying Huang;C. Lei
DOI：
10.1145/571697.571700
发表时间：
2002-07
期刊：
Comput. Commun. Rev.
影响因子：
--
作者：
K. Gummadi;S. Saroiu;S. Gribble
通讯作者：
K. Gummadi;S. Saroiu;S. Gribble