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.

翻译：本文提出了一种新型高效数字孪生（DT）数据处理方案，以降低多播短视频流的服务延迟。具体而言，构建DT来模拟和分析用户状态，用于多播组更新和滑动特征抽象。随后，建立了DT数据处理的精确测量模型，以表征DT模型大小、用户动态和用户聚类准确性之间的关系。通过考虑用户聚类准确性的影响，构建了包含DT数据处理延迟、视频转码延迟和多播传输延迟的服务延迟模型。最后，提出了DT模型大小选择与带宽分配的联合优化问题以最小化服务延迟。为高效求解该问题，提出了一种基于扩散的资源管理算法，该算法利用去噪技术改进深度强化学习算法中的动作生成过程。基于真实数据集的仿真结果表明，所提出的DT数据处理方案在服务延迟方面优于基准方案。