  We propose a digital twin-assisted adaptive preloading scheme to enhance
bandwidth efficiency and user quality of experience (QoE) in short video
streaming. We first analyze the relationship between the achievable throughput
and video bitrate and critical factors that affect the preloading decision,
including the buffer size and bitrate selection. We then construct a digital
twin-assisted adaptive preloading framework for short video streaming. By
collecting and analyzing historical throughput and tracking behavior
information, a throughput prediction model and a probabilistic model can be
constructed to accurately predict future throughput and user behavior,
respectively. Using the predicted information and real-time running status data
from a short video application, we design a preloading strategy to enhance
bandwidth efficiency while guaranteeing user QoE. Simulation results
demonstrate the effectiveness of our proposed scheme comparing with the
state-of-the-art preloading schemes.
