Multi-User Interaction Experience Optimization in the Metaverse

The Metaverse is an emerging research and business domain that aims to create a seamless integration of the physical and digital worlds. However, the current network infrastructure poses significant challenges for achieving smooth and immersive interaction in the Metaverse. In this paper, we address the problem of resource management for multi-user interaction experience optimization. We consider a scenario where users generate various types of interaction requests in the Metaverse. The Metaverse service provider (MSP) monitors the requests and accordingly, assigns the resources to the users. To capture the stochastic nature of user requests and the different interaction experience tolerance levels, we propose a novel quality-of-experience (QoE) metric and seek to maximize the user QoE by optimizing resource management. We formulate this problem as a Markov decision process (MDP) with the objective of maximizing the expected long-term user QoE. We further develop a proximal policy optimization (PPO) algorithm to solve the MDP problem. The experimental results show that the obtained PPO algorithm can significantly improve the user QoE in the Metaverse.