Traffic Profiling for Mobile Video Streaming
This provides a valuable alternative to cross-layer signaling and Deep Packet Inspection for network optimization in mobile video streaming, though it is incremental in its approach.
The paper tackles the problem of optimizing mobile video streaming by proposing a non-intrusive traffic profiling system that estimates key HTTP Adaptive Streaming parameters, such as buffer state and encoding rate, using only IP-layer information, with experimental results verifying high accuracy.
This paper describes a novel system that provides key parameters of HTTP Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A non-intrusive traffic profiling solution is proposed that observes packet flows at the transmit queue of base stations, edge-routers, or gateways. By analyzing IP flows in real time, the presented scheme identifies different phases of an HAS session and estimates important application-layer parameters, such as play-back buffer state and video encoding rate. The introduced estimators only use IP-layer information, do not require standardization and work even with traffic that is encrypted via Transport Layer Security (TLS). Experimental results for a popular video streaming service clearly verify the high accuracy of the proposed solution. Traffic profiling, thus, provides a valuable alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order to perform efficient network optimization for video streaming.