论文标题
了解野外视频流算法
Understanding video streaming algorithms in the wild
论文作者
论文摘要
虽然视频流算法是一个热门的研究领域，每隔几个月就提出了一次有趣的新方法，但对部署在大型在线流媒体平台上的流媒体算法的行为知之甚少，这些流媒体算法占互联网流量的很大一部分。因此，我们研究了在10个具有不同目标受众的视频平台上使用的自适应比特量流算法。我们收集了每个视频播放器对网络带宽中受控变化的响应的痕迹，并检查算法行为：从目标缓冲区来看，避免风险是一种算法是一种算法；启动后达到稳定状态需要多长时间；试图匹配带宽与稳定运行的反应性；它如何使用可用的网络带宽有效；等等。我们发现，部署的算法在这些轴上表现出广泛的行为，表明缺乏共识的一定大小的所有解决方案。我们还发现证据表明，大多数部署的算法都是针对稳定行为而不是快速适应带宽变化的证据，其中一些是针对视觉感知度量的，而不是基于比特率的指标，许多人留下了令人惊讶的可用带宽。
While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player's response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fits-all solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.
