Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players
Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players
复制标题
网络辅助 HTTP 自适应流媒体播放器的稳定性和比特率建模
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Pablo César
中科院分区：
文献类型：
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作者：
Jan Willem Kleinrouweler;Sergio Cabrero Barros;R. Mei;Pablo César
使用HTTP自适应流（HAS）而没有足够带宽的观众经历频繁的质量切换，这妨碍了他们的观看体验。当HAS播放器无法准确估计可用带宽时，就会产生这种称为不稳定性的情况。此外，当多个播放器在瓶颈链路上流式传输时，它们各自的适配技术可能导致信道的不公平份额。这是HAS技术中的两个有害问题，否则HAS技术是非常有吸引力的。为了克服这些问题，在文献中提出了一组解决方案，可以将其归类为网络辅助HAS。只在玩家中解决稳定性和公平性是困难的，因为玩家对网络的看法有限。使用来自网络设备的信息可以帮助玩家做出更好的适应决策。本文的贡献有三个方面。首先，我们描述了我们的实现中的HTTP代理服务器的形式，并表明，稳定性和公平性大大提高。其次，我们提出了一个分析模型，允许计算视频质量和视频流的比特率的变化的数量。第三，我们通过比较基于模型的视频质量变化数量和视频流平均比特率的估计，验证了模型的准确性，并在我们的HAS助手的真实的实现中得到了结果。结果表明，基于模型的结果是高度准确的。因此，该模型在实践中对于规划使用网内HAS助理的视频递送网络是有用的，并且使我们能够在真实的部署之前分析HAS流的稳定性和平均比特率。
Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the model-based estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.