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Modern connected vehicles (CVs) frequently require diverse types of contentfor mission-critical decision-making and onboard users' entertainment. Thesecontents are required to be fully delivered to the requester CVs withinstringent deadlines that the existing radio access technology (RAT) solutionsmay fail to ensure. Motivated by the above consideration, this paper exploitscontent caching in vehicular edge networks (VENs) with a software-defineduser-centric virtual cell (VC) based RAT solution for delivering the requestedcontents from a proximity edge server. Moreover, to capture the heterogeneousdemands of the CVs, we introduce a preference-popularity tradeoff in theircontent request model. To that end, we formulate a joint optimization problemfor content placement, CV scheduling, VC configuration, VC-CV association andradio resource allocation to minimize long-term content delivery delay.However, the joint problem is highly complex and cannot be solved efficientlyin polynomial time. As such, we decompose the original problem into a cacheplacement problem and a content delivery delay minimization problem given thecache placement policy. We use deep reinforcement learning (DRL) as a learningsolution for the first sub-problem. Furthermore, we transform the delayminimization problem into a priority-based weighted sum rate (WSR) maximizationproblem, which is solved leveraging maximum bipartite matching (MWBM) and asimple linear search algorithm. Our extensive simulation results demonstratethe effectiveness of the proposed method compared to existing baselines interms of cache hit ratio (CHR), deadline violation and content delivery delay.

Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands