Resource Allocation for Vehicle-Satellite-Cloud Empowered Vehicular Edge Metaverse
Caiguo Li, Bodong Shang, Yanhui Wu · 2024
Combining the metaverse and vehicular edge computing (VEC) as two cutting-edge technological fields shows multiple new applications and creations. However, the existing vehicular metaverse edge computing (VMEC) schemes may not guarantee the quality of experience (QoE) of vehicular metaverse users (VMU) due to the limitations of terrestrial network coverage and insufficient computing resources. This paper introduces a novel VMEC scheme based on a threetier vehicle-satellite-cloud integrated network. This innovative approach offloads VMU's task and transmits a portion of the task to the ground cloud server (CS) for cooperative offloading, thereby enhancing the overall system performance. By leveraging the real-time data transmission capabilities of satellites and the robust processing power of CS, our proposed scheme holds the promise of significantly improving QoE, i.e., the weighted sum of metaverse data size and offloading latency, in VMEC. Specifically, we consider task partition and computing resources to maximize the QoE of VMUs. Moreover, we study an iterative algorithm by decomposing the original problem into several sub-problems, and we solve each sub-problem using the optimization tool and the Lagrange dual method. Simulation results demonstrate that our scheme outperforms existing schemes in terms of VMUs' QoE.