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.

翻译：组播短视频流媒体技术通过向多用户同时传输相同的视频序列，可有效降低网络流量负载。现有组播短视频方案主要依赖聚合视频请求来预留带宽与计算资源，但难以满足用户多样化的动态服务需求，尤其当用户的滑动行为呈现时空波动性时。本文提出一种基于数字孪生技术的用户中心化资源管理方案，旨在提升用户满意度的同时降低资源消耗。首先，我们设计用户数字孪生辅助的资源预留框架。具体而言，为每个用户构建用户数字孪生体，存储用户历史数据用于更新组播组及提取有用信息。从用户数字孪生中提取滑动概率分布和推荐视频列表，以预测带宽与计算资源需求，并利用参数化Sigmoid函数刻画组播组用户满意度。其次，我们构建一个联合非凸的带宽与计算资源预留问题，通过引入正切函数近似替换凹函数部分，将其转化为凸分段问题。随后开发一种低复杂度调度算法，求解最优资源预留决策。基于真实数据集的仿真结果表明，所提方案在用户满意度和资源消耗方面均优于基准方案。