Human-centric VR task offloading in metaverse-enabled wireless-powered heterogeneous MEC networks

Metaverse-enabled multi-access edge computing (MEC) integrates the metaverse with MEC by deploying user task requests for metaverse resources and scenes at the network edge, thereby enhancing users’ immersive experience. When accessing the metaverse via virtual reality (VR) devices, users require high real-time performance and diverse computing resources. Additionally, wireless charging is adopted in metaverse-enabled MEC to improve device mobility and avoid experience interruptions caused by battery depletion. Meanwhile, distributed computing, which leverages the spatial distribution of computing, storage, and communication resources across multiple edge nodes, addresses the demands of large-scale interactive scenes and heterogeneous infrastructures in the metaverse, boosting system scalability and reducing processing latency through task allocation and resource sharing. However, inefficient task offloading strategies and suboptimal charging decisions severely degrade network efficiency and user experience such as resource waste, user discomfort and dizziness, highlighting key challenges in task offloading and wireless charging for metaverse-enabled MEC. To address these issues, we formulate a human-centric optimization problem for VR task offloading and wireless charging in wireless-powered heterogeneous metaverse-enabled MEC networks, prioritizing the enhancement of user physical comfort and access point (AP) charging efficiency. To tackle this problem, we propose a framework of the metaverse-enabled MEC Deep Reinforcement Learning (DRL)-based Hybrid Proximal Policy Optimization (MetaMEC-HPPO) with K-Means algorithm preconditioning the network. Furthermore, the experimental results show that the proposed algorithm can achieve superior convergence performance and, meanwhile, improve users’ physical comfort and charging profit of AP devices efficiently compared to other baseline approaches. In particular, the average reward of our proposed MetaMEC-HPPO is 11.4% higher than that of Asynchronous Advantage Actor–Critic (A3C) method.