Joint Optimization of Cache Allocation and Content Placement in Urban Vehicular Networks

Distributing popular contents, e.g., high precision digital maps and latest road conditions, through roadside units (RSUs) is a promising way to provide better driving safety and support for autonomous driving. Successful content download probability can be improved by allocating cache to RSUs and caching popular contents therein. In this paper, we consider a joint cache allocation and content placement problem in vehicular networks to maximize the overall successful content download probability, exploiting the moving information of vehicles. We first prove that the original problem is NP-hard, and then propose a low-complexity approximate algorithm which performs within a bounded gap (a multiplicity factor of 1 -1/ e) to the optimum. The expression of the cache size allocated to each RSU is derived for the proposed algorithm when the content popularity obeys Zipf distribution. Extensive numerical experiments show that, the proposed strategy can significantly increase the successful content download probability as compared to existing solutions.