QoE-Aware Resource Allocation in Mobile Edge Computing Enabled Vehicular Metaverse

In this study, we propose a mobile edge computing (MEC)-enabled vehicular Metaverse system designed for augmented reality (AR) services, where vehicles on the road can access the Metaverse service through nearby Metaverse service providers (MSPs) equipped with MEC servers. In this system, vehicles are charged for their use of computational and communication resources. Due to varying positions and viewing angles, vehicles may have different content preferences. To minimize cost while ensuring optimal quality of experience (QoE), we formulate an optimization problem that adjusts content resolution and resource allocation to match individual vehicle needs. To address this optimization problem, we introduce a deep reinforcement learning (DRL) algorithm integrated with active inference theory to solve the decision-making problem with the performance of low latency and high efficiency. Simulation results demonstrate that our proposed scheme outperforms comparative algorithms in comprehensive performance, providing an effective solution for optimizing AR-enabled vehicular Metaverse systems.