Joint computation offloading and resource allocation in vehicular edge computing networks

Vehicular Edge Computing (VEC) is a promising technique to accommodate the computation-intensive and delaysensitive tasks through offloading the tasks to the RoadSide-Unit (RSU) equipped with edge computing servers or neighboring vehicles. Nevertheless, the limited computation resources of edge computing servers and the mobility of vehicles make the offloading policy design very challenging. In this context, through considering the potential transmission gains brought by the mobility of vehicles, we propose an efficient computation offloading and resource allocation scheme in VEC networks with two kinds of offloading modes, i.e., Vehicle to Vehicle (V2V) and Vehicle to RSU (V2R). We define a new cost function for vehicular users by incorporating the vehicles' offloading delay, energy consumption, and expenses with a differentiated pricing strategy, as well as the transmission gain. An optimization problem is formulated to minimize the average cost of all the task vehicles under the latency and computation capacity constraints. A distributed iterative algorithm is proposed by decoupling the problem into two subproblems for the offloading mode selection and the resource allocation. Matching theorybased and Lagrangian-based algorithms are proposed to solve the two subproblems, respectively. Simulation results show the proposed algorithm achieves low complexity and significantly improves the system performance compared with three benchmark schemes.