A Unified Framework for Automatic Quality-of-Experience Optimization in Mobile Video Streaming

Mobile video streaming is one of the fastest growing applications in the mobile Internet. Nevertheless, delivering high-quality streaming video over mobile networks remains a challenge. Researchers have since developed various novel streaming algorithms such as rate-adaptive streaming to improve the performance of mobile streaming services. However, selection or optimization of streaming algorithms is far from trivial and there is no systematic way to incorporate the tradeoffs between various performance metrics. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) to automatically tune any streaming algorithms to maximize a given quality-of-experience (QoE) objective. We show that PSQA not only can be applied to optimize the performance of existing streaming algorithms, but also opens a new way for the exploration of new adaptive video streaming protocols and QoE metrics. Simulation results based on real network throughput traces show that PSQA can optimize existing and new streaming algorithms to achieve QoE that is remarkably close to the optimal achieved using brute-force method ex post facto.