Utility maximization of cloud-based in-car video recording over vehicular access networks

With the advance of cloud computing and 4G/5G technology, video contents recorded by in-car cameras (i.e., vehicular digital video recorders) can be uploaded to the cloud to facilitate accident analysis, online surveillance, video sharing, etc. However, the cost of uploading such huge volume of video contents via unstable vehicular access networks (including cellular base stations and road-side units) can be considerable by considering the increasing video quality requirement, time constraint, and limited local buffer space. In this paper, we propose an adaptive video recording and uploading scheme to maximize the overall utility of cloud-based in-car video uploading over vehicular access networks. Specifically, the utility function is defined as the weighted sum of bandwidth cost and video quality and we formulate the problem into a constrained Markov decision process (MDP). Based on the theoretic foundation of MDP, we design and implement an algorithm to obtain an adaptive chunk uploading policy for video contents over vehicular access networks. Extensive simulations have been conducted to demonstrate that our policy can achieve the best performance compared with other alternative strategies.