加密HTTP自适应视频流的实时的视频质量表示分类--以Safari为例
Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari
摘要：
HTTP自适应视频流服务的日益普及大大增加了运营商网络的带宽要求，运营商网络试图通过深度数据包检测（DPI）来塑造其流量。不过，谷歌和某些内容提供方已经开始对他们的视频服务进行加密。因此，运营商在通过DPI塑造其加密视频流量时经常遇到困难。这突出了对加密HTTP自适应视频流的新流量分类方法的需求，以实现智能流量整形。这些新方法必须有效地估计质量表示层和播放缓冲区。我们提出了一种新的方法，并首次表明（YouTube）加密HTTP自适应流的视频质量表示分类是可能的。我们分析了这种分类方法与Safari在HTTPS上的性能。基于大量的离线和在线流量分类实验，我们证明了它可以独立地分类，在实时的，每个视频片段到一个质量表示层，平均准确率为97.18%。
英文摘要：
The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. We analyze the performance of this classification method with Safari over HTTPS. Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 97.18% average accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
