QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks
QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks
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DOI：
10.1109/glocom.2012.6503401
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发表时间：
2012-12
影响因子：
7.3
通讯作者：
Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;A. Khisti
中科院分区：
文献类型：
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作者：
Weiwen Zhang;Yonggang Wen;Zhenzhong Chen;A. Khisti
在本文中，我们研究的HTTP自适应比特率（ABR）流在无线网络上的最佳内容缓存管理的问题。具体地，在媒体云中，每个内容被转码成具有不同回放速率的一组媒体文件，并且将响应于频道条件和屏幕形式动态地选择适当的文件。我们的设计目标是在有限的存储预算下，为最终用户最大限度地提高单个内容的体验质量（QoE）。从我们的实验结果推导出一个对数QoE模型，我们制定了个人的内容缓存管理HTTP ABR流在无线网络上作为一个约束凸优化问题。我们采用两步的过程来解决快照问题。首先，使用拉格朗日乘子方法，我们获得了一个固定数量的缓存副本的播放速率的数值解，并分析了最优解的特征。我们的调查揭示了一个基本的相位变化的最佳解决方案的高速缓存文件的数量增加。其次，我们开发了三种替代搜索算法，以找到最佳数量的缓存文件，并比较其可扩展性下的平均和最差的复杂性指标。我们的数值结果表明，在最佳缓存方案下，最大QoE测量，即，平均意见得分（MOS）是允许存储大小的凹函数。我们的缓存管理可以提供高预期的QoE与低复杂度，揭示了在无线网络上的HTTP ABR流媒体服务的设计。
In this paper, we investigate the problem of optimal content cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks. Specifically, in the media cloud, each content is transcoded into a set of media files with diverse playback rates, and appropriate files will be dynamically chosen in response to channel conditions and screen forms. Our design objective is to maximize the quality of experience (QoE) of an individual content for the end users, under a limited storage budget. Deriving a logarithmic QoE model from our experimental results, we formulate the individual content cache management for HTTP ABR streaming over wireless network as a constrained convex optimization problem. We adopt a two-step process to solve the snapshot problem. First, using the Lagrange multiplier method, we obtain the numerical solution of the set of playback rates for a fixed number of cache copies and characterize the optimal solution analytically. Our investigation reveals a fundamental phase change in the optimal solution as the number of cached files increases. Second, we develop three alternative search algorithms to find the optimal number of cached files, and compare their scalability under average and worst complexity metrics. Our numerical results suggest that, under optimal cache schemes, the maximum QoE measurement, i.e., mean-opinion-score (MOS), is a concave function of the allowable storage size. Our cache management can provide high expected QoE with low complexity, shedding light on the design of HTTP ABR streaming services over wireless networks.