VIDTRA: An Efficient and Resilient Video Preloading System

With the increasing demand for video-based applications, the clarity and fluidity of videos have garnered widespread attention. Current video playback mechanisms either allocate resources to clients with good channel quality or maintain video playback continuity at low bit rates, affecting the user viewing experience. In this work, we propose a video preloading system, namely VIDTRA, which departs from the current paradigm and explores a resilient design scheme. The central insight in VIDTRA is to utilize network situation maps and user trajectory prediction to forecast the serving cell and received signal strength, thereby determining the duration of video preloading. Considering the predictability of public transport route trajectories, our system is primarily designed to enhance the video watching experience for users on public transport, which encompasses functionalities such as route clustering and the identification of users’ boarding and alighting. Using real-world data collected from user equipments, we thoroughly evaluate and demonstrate the efficacy of VIDTRA. Results from the experimental evaluations show that VIDTRA can precisely estimate the future signal strength received by users and initiate video preloading before they enter areas with weak signal quality, thus reducing the interruptions of the video while guaranteeing the high definition.