Joint optimization of resource allocation and computation offloading based on game coalition in C-V2X

Abstract

In order to solve the problem of limited resources in the vehicle, mobile edge computing (MEC) is integrated into the cellular-vehicle-to-everything (C-V2X) system to provide users with low-latency services. Considering the resource limitation of computing, storage and communication of MEC servers, it is necessary to improve the system performance through the collaborative optimization of multi-domain resources. Therefore, for applications with double dependence on time and data in C-V2X scenarios, we design a MEC hierarchical resource management framework to jointly optimize system offloading decision, scheduling decision, and caching decision to minimize the system delay. The optimization problem is decomposed into a resource allocation problem within a single MEC server and a load balancing problem among multiple MEC servers. For the former, we present a task scheduling algorithm based on the latest start time and a caching decision algorithm based on dynamic programming to minimize the average task completion delay; for the latter, we present a load balancing algorithm based on the coalition game to minimize the global system delay. Simulation results show that the proposed scheme can significantly improve the performance in terms of application completion delay, application failure rate and resource utilization compared with the benchmark schemes.