Duration-adaptive Video Highlight Pre-caching for Vehicular Communication Network

Liang Xu, Deshi Li, Kaitao Meng, Mingliu Liu, Shuya Zhu

Abstract: Video traffic in vehicular communication networks (VCNs) faces exponential growth. However, different segments of most videos reveal various attractiveness for viewers, and the pre-caching decision is greatly affected by the dynamic service duration that edge nodes can provide services for mobile vehicles driving along a road. In this paper, we propose an efficient video highlight pre-caching scheme in the vehicular communication network, adapting to the service duration. Specifically, a highlight entropy model is devised to balance the segments’ popularity and continuity between segments within a period of time, based on which, an optimization problem of video highlight pre-caching is formulated. As this problem is non-convex and lacks a closed-form expression of the objective function, we decouple multiple variables by deriving candidate highlight segmentations of videos through wavelet transform, which can quickly and accurately find chunks with peak popularity values. Then the problem is solved iteratively by a highlight-direction trimming algorithm, which is proven to be locally optimal. Simulation results based on a real-world video dataset demonstrate significant improvement in highlight entropy and jitter compared to benchmark schemes.