  In this paper, we propose a novel efficient digital twin (DT) data processing
scheme to reduce service latency for multicast short video streaming.
Particularly, DT is constructed to emulate and analyze user status for
multicast group update and swipe feature abstraction. Then, a precise
measurement model of DT data processing is developed to characterize the
relationship among DT model size, user dynamics, and user clustering accuracy.
A service latency model, consisting of DT data processing delay, video
transcoding delay, and multicast transmission delay, is constructed by
incorporating the impact of user clustering accuracy. Finally, a joint
optimization problem of DT model size selection and bandwidth allocation is
formulated to minimize the service latency. To efficiently solve this problem,
a diffusion-based resource management algorithm is proposed, which utilizes the
denoising technique to improve the action-generation process in the deep
reinforcement learning algorithm. Simulation results based on the real-world
dataset demonstrate that the proposed DT data processing scheme outperforms
benchmark schemes in terms of service latency.
