# Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming

arXiv: https://arxiv.org/abs/2308.08995 · HTML: https://mlsanity.com/paper/2308.08995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2023.

## Built on by (most cited first, 4 of 4)

- [Integrating Generative AI with Network Digital Twins for Enhanced Network Operations](https://mlsanity.com/paper/2406.17112/) (2024)
- [Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles](https://mlsanity.com/paper/2407.08047/) (2024)
- [Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metric](https://mlsanity.com/paper/2502.17983/) (2025)
- [Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins](https://mlsanity.com/paper/2602.18627/) (2026)

## Abstract (arXiv)

Multicast short video streaming (MSVS) can effectively reduce network traffic load by delivering identical video sequences to multiple users simultaneously. The existing MSVS schemes mainly rely on the aggregated video requests to reserve bandwidth and computing resources, which cannot satisfy users' diverse and dynamic service requirements, particularly when users' swipe behaviors exhibit spatiotemporal fluctuation. In this paper, we propose a user-centric resource management scheme based on the digital twin (DT) technique, which aims to enhance user satisfaction as well as reduce resource consumption. Firstly, we design a user DT (UDT)-assisted resource reservation framework. Specifically, UDTs are constructed for individual users, which store users' historical data for updating multicast groups and abstracting useful information. The swipe probability distributions and recommended video lists are abstracted from UDTs to predict bandwidth and computing resource demands. Parameterized sigmoid functions are leveraged to characterize multicast groups' user satisfaction. Secondly, we formulate a joint non-convex bandwidth and computing resource reservation problem which is transformed into a convex piecewise problem by utilizing a tangent function to approximately substitute the concave part. A low-complexity scheduling algorithm is then developed to find the optimal resource reservation decisions. Simulation results based on the real-world dataset demonstrate that the proposed scheme outperforms benchmark schemes in terms of user satisfaction and resource consumption.