Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Abstract

Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.

Keywords

• QoE
• multiagent
• reinforcement learning
• edge computing