Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

In this paper, we propose a cross-layer decision framework for multiuser adaptive video delivery over time-varying and mutually interfering wireless cellular network. The key idea is to synthetically design the physical-layer optimization-based beamforming scheme (performed at the base stations) and the application-layer deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals), so that a very complex multi-user overall fair long-term quality of experience (QoE) maximization problem can be decomposed to two layers and solved effectively. Extensive simulations show that the proposed cross-layer design is effective and promising.