Classifying flows and buffer state for YouTube's HTTP adaptive streaming service in mobile networks
This provides a tool for monitoring and managing encrypted video traffic in mobile networks, addressing a challenge due to encryption and lack of cross-layer standards, though it is incremental as it builds on existing traffic profiling methods.
The paper tackled the problem of obtaining application-layer information for optimizing mobile networks by developing a traffic profiling solution that uses machine learning to identify video flows and detect the playback buffer state of HTTP Adaptive Streaming applications from IP packet arrivals, achieving very high accuracy with Random Forests even under varying link conditions.
Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has become very difficult due to the wide adoption of end-to-end encryption and due to the absence of cross-layer signaling standards. As an alternative, this paper presents a traffic profiling solution to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications at the lower layers. By observing IP packet arrivals, our machine learning system identifies video flows and detects the state of an HAS client's play-back buffer in real time. Our experiments with YouTube's mobile client show that Random Forests achieve very high accuracy even with a strong variation of link quality. Since this high performance is achieved at IP level with a small, generic feature set, our approach requires no Deep Packet Inspection (DPI), comes at low complexity, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for monitoring and managing even encrypted HAS traffic in mobile networks.