Factor Selection for Reinforcement Learning in HTTP Adaptive Streaming

At present, HTTP Adaptive Streaming (HAS) is developing into a key technology for video delivery over the Internet. In this delivery strategy, the client proactively and adaptively requests a quality version of chunked video segments based on its playback buffer, the perceived network bandwidth and other relevant factors. In this paper, we discuss the use of reinforcement-learning (RL) to learn the optimal request strategy at the HAS client by progressively maximizing a pre-dened Quality of Experience (QoE)-related reward function. Under the framework of RL, we investigate the most influential factors for the request strategy, using a forward variable selection algorithm. The performance of the RL-based HAS client is evaluated by a Video-on-Demand (VOD) simulation system. Results show that given the QoE-related reward function, the RL-based HAS client is able to optimize the quantitative QoE. Comparing with a conventional HAS sys- tem, the RL-based HAS client is more robust and flexible under versatile network conditions.