Digital Twin-Assisted Adaptive Preloading for Short Video Streaming

We propose a digital twin-assisted adaptive preloading scheme to reduce bandwidth waste as well as enhance user quality of experience (QoE) for short video streaming. Though preloading video content can reduce rebuffering and improve user QoE, non-sequential playback of short videos induced by user swipe can result in substantial bandwidth wastage in mobile networks. To tackle this problem, we first model the short video streaming system and carry out preloading threshold analysis. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing the user's historical throughput and tracking swipe timing information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user swipe behavior, respectively. Utilizing the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while achieving high user QoE. Simulation results demonstrate the effectiveness of our proposed scheme compared with the state-of-the-art schemes.