Joint Road Side Units Selection and Resource Allocation in Vehicular Edge Computing

With powerful storage and computation capability, vehicular edge computing is considered as a promising paradigm to enhance the safety and quality-of-service of vehicles in intelligent transportation systems. In this work, we investigate a joint road side units (RSUs) selection and resource allocation problem, with the objective of minimizing the total task offloading delay subject to the bandwidth and computation resources constraints, in non-collaborative and collaborative RSU scenarios. For the former, as the formulated problem is a mixed-integer nonlinear programming problem, we re-formulate the original problem into a convex one and then decompose it into a distributed manner. By utilizing the alternating direction method of multipliers, we propose a joint RSUs selection and resource allocation (JRSRA) algorithm. For the collaborative RSU scenarios, we transform the problem into a convex optimization problem by linearization, and propose a joint resource allocation based branch and bound (JRABB) algorithm to solve the total task offloading delay minimization problem. Simulation results show that the proposed JRSRA and JRABB algorithms can reduce the total task offloading delay compared with other benchmark methods.