Efficient Digital Twin Data Processing for Low-Latency Multicast Short Video Streaming

TL;DR

This paper introduces an efficient digital twin data processing scheme that reduces latency in multicast short video streaming by optimizing model size and bandwidth allocation through a novel joint optimization and diffusion-based algorithm.

Contribution

It presents a new digital twin data processing framework with a measurement model and a joint optimization approach, improving latency performance in multicast video streaming.

Findings

Outperforms benchmark schemes in reducing service latency

Effective digital twin model size and bandwidth allocation strategy

Enhanced resource management with diffusion-based deep reinforcement learning

Abstract

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.