A QoE-based Optimization Approach to Computation Offloading in Vehicle-assisted Multi-access Edge Computing

In recent years, multi-access edge computing (MEC) has been proposed to provide low-latency computing services for mobile devices (MDs) by enabling computation task offloading from MDs to nearby MEC servers. However, the computing resources of a MEC server is usually limited and unable to satisfy the ever-increasing demand of task requests. To tackle this challenge, in this paper, we investigate a vehicle-assisted MEC (vMEC) paradigm, which expands the current available resources of a MEC server by integrating it with the resources of vehicular nodes (VNs) for task offloading from MDs. We propose a new utility optimization approach that factors in quality of experience (QoE) metrics to address the problem of computation offloading in vMEC. Simulation results validate the advantage of our proposal.