###### 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.  
QoE; multiagent; reinforcement learning; edge computing +
Footnote †: journal: _Future Internet_  
0000-0001-9999-  
by these two solutions to server assignment and bitrate adaption vary, they both strive to improve user QoE while utilizing the least amount of infrastructure resources or system costs. These two issues are typically framed as combinatorial optimization problems and resolved separately one after the other. However, these two issues are strongly interrelated, and we are motivated to propose an end-to-end approach based on multiagent reinforcement learning (MARL) that integrates the above two problems, which treats each edge server as an agent for policy execution and improves the policy through feedback from users' experience, thereby continuously improving the quality of service of all edge servers. To better realize the cooperative relationship between edge servers, we achieve information sharing among edge servers by introducing an attention mechanism, then obtaining the final service policy through centralized training and decentralized execution.  
The main contributions of this article are summarized as follows:  
* We model the problems of user allocation and bitrate selection as a decentralized partially observable Markov decision process (Dec-POMDP).
* We propose an end-to-end approach based on multiagent reinforcement learning that can simultaneously consider the edge user allocation and bitrate selection problems.
* We use the attention mechanism to achieve the information sharing among edge servers, then obtain the final service policy through centralized training and decentralized execution.  
Therefore, an attention-based MARL (AMARL) is designed to tackle the EUA problem and bitrate selection simultaneously under a dynamic environment. The remainder of the article is organized as follows: The literature is explored in Section 2. The details of the method are described in Section 3. The experiments are implemented in Section 4. Finally, we conclude this paper in Section 5.