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Accurate cross-layer information is very useful to optimize mobile networksfor specific applications. However, providing application-layer information tolower protocol layers has become very difficult due to the wide adoption ofend-to-end encryption and due to the absence of cross-layer signalingstandards. As an alternative, this paper presents a traffic profiling solutionto passively estimate parameters of HTTP Adaptive Streaming (HAS) applicationsat the lower layers. By observing IP packet arrivals, our machine learningsystem identifies video flows and detects the state of an HAS client'splay-back buffer in real time. Our experiments with YouTube's mobile clientshow that Random Forests achieve very high accuracy even with a strongvariation of link quality. Since this high performance is achieved at IP levelwith a small, generic feature set, our approach requires no Deep PacketInspection (DPI), comes at low complexity, and does not interfere withend-to-end encryption. Traffic profiling is, thus, a powerful new tool formonitoring and managing even encrypted HAS traffic in mobile networks.

Classifying flows and buffer state for YouTube's HTTP adaptive streaming service in mobile networks