Reinforcement learning method for QoE-aware optimization of content delivery

The delivery of video services in a controllable and resource efficient manner while meeting the various QoE/QoS requirements in mobile networks is a challenging task, especially in a multiclass wireless environment. This paper proposes an intelligent and context-aware application level fair scheduler based on reinforcement-learning, which can dynamically adjust relevant scheduling parameters in reaction to specific events or context information. The implemented Q-learning method is analyzed with reference to the delivery of progressive video streaming services. We first highlight the performance issues during progressive video streaming over TCP to multiple users under resource constrained environment. We then demonstrate the utilization of employing Q-learning method in our scheduler for intelligent orchestration between multiple concurrent flows to ensure against buffer starvation and thus enable smooth playback. We also demonstrate the effectiveness of our context-aware dynamic scheduler to provide service separation between the user classes and fairness within a user class.