  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.
