Multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users' swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs' buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.

翻译：多播短视频流媒体能够通过共享无线信道同时向多个用户传输视频，从而提高带宽利用率。现有网络管理方案主要依赖顺序缓冲原则和通用用户体验质量模型，当用户滑动行为呈现显著时空变化时，可能导致用户体验质量恶化。本文提出了一种基于数字孪生的网络管理方案以提升用户体验质量。首先，利用数字孪生模拟的用户状态估计子多播组的传输能力与观看概率分布，实现自适应分片缓冲。子多播组的缓冲区与数字孪生管理的独特虚拟缓冲区对齐，实现细粒度缓冲更新。其次，通过考虑子多播组间分片缓冲的相互影响，构建了包含重缓冲时间、视频质量与质量变化的多播用户体验质量模型。最后，建立分片版本选择与时隙划分的联合优化问题以最大化用户体验质量。为高效求解该问题，提出了一种融合凸优化方法与深度强化学习算法的数据-模型混合驱动算法。基于真实数据集的仿真结果表明，相较于基准方案，所提基于数字孪生的网络管理方案能更有效地提升用户体验质量。