Adaptive Video Segment Pre-Caching With Varying Travel Duration for Internet of Vehicles

With the rapid expansion of autonomous vehicles and entertainment applications, video traffic in the Internet of Vehicles (IoV) faces exponential growth. This surge in video demand presents significant challenges for effective pre-caching strategies, particularly due to high vehicle mobility and heterogeneous dwell times at edge nodes caused by varying speeds. In this paper, we propose an efficient adaptive video segment pre-caching scheme (AVSC) for the IoV, addressing varying travel durations of vehicles on road. Specifically, we develop two video evaluation models to balance the popularity of cached video segments with the fidelity of their distribution across the entire video, ensuring temporal continuity. Then, a multi-objective optimization problem is formulated to jointly maximize highlight entropy and segment distribution fidelity. By leveraging the time-frequency characteristics of the wavelet transform, initial segment candidates are identified by detecting significant changes in the time series of chunk popularity (derived from analyzing frame-level popularity). This approach reduces the search space and computation time for subsequent segment selection. Based on the initial segment candidates, the highlight-direction optimal algorithm is proposed to iteratively identify highlight candidates by improving highlight entropy. For Pareto-optimal solutions, a caching-segment adjustment algorithm based on neighborhood search is proposed to determine the final cached video segments. Theoretical guarantees are provided for the identification process. Furthermore, the adjustment algorithm is proven to detect the maximal improvement direction of distribution fidelity, enhancing convergence speed. Simulations on real-world video datasets demonstrate the effectiveness of the proposed AVSC.