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Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players

机译:网络辅助HTTP自适应流播放器的稳定性和比特率建模

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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.
机译:使用HTTP自适应流(HAS)且带宽不足的观看者会经历频繁的质量切换,从而阻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的情况。此外,当几个播放器流经瓶颈链路时,他们各自的适应技术可能会导致信道分配不公平。这是HAS技术中的两个有害问题,否则它们将非常有吸引力。为了克服它们,文献中提出了一组解决方案,这些解决方案可以归类为网络辅助的HAS。仅在播放器中解决稳定性和公平性是困难的,因为播放器对网络的视野有限。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。本文的贡献是三方面的。首先,我们以HTTP代理服务器的形式描述我们的实现,并表明稳定性和公平性都得到了极大的提高。其次,我们提出一种分析模型,该模型可以计算视频质量变化的数量和视频流的比特率。第三,我们通过比较基于模型的视频质量变化次数和视频流平均比特率的估计值来验证模型的准确性,从而真正实现我们的HAS助手。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计划使用网络内HAS助手的视频传递网络很有用,并且使我们能够在实际部署之前分析HAS流的稳定性和平均比特率。

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