Data-driven clustering for multimedia communication in Internet of vehicles

The main requirement of multimedia communication service is to improve ultra-reliable and low-latency data communication, so the challenge of providing multimedia communication service is to improve data sharing for making full use of network resources. In this paper, a data content based vehicle clustering model is designed to analyze the transmitted multimedia data correlation between the vehicles, and the vehicles with high correlation of transmitted multimedia data are classified into a cluster and share the same resources. The data sharing in network is regarded as a performance criterion for adjusting the self-organized multimedia communication structure. Based on these factors, in order to ensure the stability of the self-organized communication structure, this paper proposes a content-aware stable multimedia communication algorithm for Internet of vehicles, which controls the multimedia communication within a certain range and combines with the transmitted multimedia data correlation of the vehicles that need to be adjusted. Finally, a network clustering structure with data sharing maximization and stable multimedia communication is performed. Extensive simulation experiments are carried out to evaluate the performance of the proposed algorithm in terms of network stability, average end-to-end communication delay, and packet loss rate.