Resource Allocation and QoE Maximization in Aerial MEC-empowered Metaverse Service: A CCM-Multi-agent DRL approach

The integration of Mobile Edge Computing (MEC) with aerial platforms introduces novel potential for the Metaverse world by providing low-latency and highly reliable computing and communication services at the network edge. Nevertheless, this integration presents critical challenges, such as low Quality of Experience (QoE) due to the dynamic nature of aerial platforms, high resource demands, and the requirements for real-time data processing in the Metaverse environment. To address these challenges, we propose a Combinatorial Client-Master Multiagent Deep Reinforcement Learning (CCM-MADRL) based joint resource allocation and QoE maximization framework to enable intelligent real-time decision-making in aerial MEC enabled Metaverse services. We form a collaborative ecosystem where agents are designed to represent both Metaverse service providers and aerial platforms to promote fairness and efficiency in resource allocation, as well as optimize service delivery. By incorporating CCM, our approach considers diverse metrics, such as latency, reliability, meta-distance, and energy efficiency, to ensure a holistic optimization of Metaverse services. The MADRL approach enables adaptive decision-making, allowing the system to respond to the dynamic and unpredictable nature of Metaverse applications. Results from simulations that mimic realistic Metaverse scenarios demonstrate the effectiveness of the proposed CCM-MADRL framework in terms of improved service performance, reduced latency, cost, and virtual meta-distance, maximized average QoE utility of Metaverse users, and enhanced resource utilization compared to baseline algorithms.