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

TL;DR

This paper introduces a duration-adaptive pre-caching scheme for vehicular networks that optimizes video highlight caching based on service duration, segment popularity, and continuity, improving cache efficiency.

Contribution

It proposes a novel highlight entropy model and an iterative highlight-direction trimming algorithm for efficient, adaptive video highlight pre-caching in vehicular communication networks.

Findings

Significant improvement in highlight entropy over benchmarks

Reduction in jitter and cache inefficiency

Effective adaptation to dynamic service durations

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 with the consideration of 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 significantly reduce the complexity of highlight pre-caching. Then the problem is solved iteratively by a highlight-direction trimming algorithm, which is proven to be locally optimal. Simulation results based on real-world video datasets demonstrate significant improvement in highlight entropy and jitter compared to benchmark schemes.