Network Coding-Assisted Data Broadcast in Large-Scale Vehicular Networks

This chapter studies on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in large-scale vehicular networks. In particular, we consider the scenario where vehicles request a set of information, and they could be served via heterogeneous wireless interfaces. We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature-inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm (MA), which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.