Dynamic Bitrate Adaptation and Bandwidth Allocation for MEC-Enabled Video Streaming

This letter investigates a video streaming system enabled by mobile edge computing (MEC), where we aim to maximize the user quality of experience (QoE) under dynamic mobile environments, through user association, bitrate adaptation and bandwidth allocation. To tackle this hybrid optimization problem, we first decompose it into discrete inter-frame and continuous intra-frame problems. In further, an in-depth analysis of intra-frame bandwidth allocation is conducted, guiding the design of a modified equal bandwidth allocation (MEBA) algorithm. Furthermore, we adopt a deep reinforcement learning (DRL) approach to cope with inter-frame user association and bitrate adaptation in dynamic mobile environments, while embedding MEBA in the DRL algorithm to jointly tackle the hybrid problem. Simulations are finally conducted to verify the superiority of the proposed method under the dynamic environment.