Video semantics-driven resource allocation algorithm in Internet of vehicles - Jiujiu CHEN; Chunyan FENG; Caili GUO; Yang YANG; Qizheng SUN; Meiyi ZHU (Tongxin xuebao)
Aiming at the problem that traditional resource allocation methods will no longer be applicable, with the demand of intelligent computing services such as video semantic understanding in Internet of vehicles, the video semantic driven resource allocation algorithm was studied.First of all, taking the object detection task as an example, a semantic driven resource allocation guidance model for video was proposed and an algorithm for solving model parameters was given.Secondly, an optimization problem of resource allocation driven by video semantics in Internet of vehicles was constructed, which was transformed into a convex problem and solved by convex optimization algorithm.Furthermore, in order to reduce the complexity of the convex optimization algorithm, a resource allocation algorithm based on reinforcement Q learning was proposed.Finally, the performance advantages of the proposed algorithm are verified by simulations.