Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks
This addresses video streaming performance and energy efficiency for mobile users in heterogeneous networks, but it is incremental as it builds on existing prediction and prefetching techniques.
The paper tackles the problem of mobile video streaming by using mobility and throughput prediction to prefetch data in integrated cellular-WiFi networks, resulting in reduced paused video frames and energy consumption as evaluated through trace-driven simulations.
We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.