  In this paper, we propose a digital twin (DT)-assisted resource demand
prediction scheme to enhance prediction accuracy for multicast short video
streaming. Particularly, we construct user DTs (UDTs) for collecting real-time
user status, including channel condition, location, watching duration, and
preference. A reinforcement learning-empowered K-means++ algorithm is developed
to cluster users based on the collected user status in UDTs, which can
effectively employ the mined users' intrinsic correlation to improve the
accuracy of user clustering. We then analyze users' video watching duration and
preferences in each multicast group to obtain the swiping probability
distribution and recommended videos, respectively. The obtained information is
utilized to predict radio and computing resource demand of each multicast
group. Initial results demonstrate that the proposed scheme can effectively
abstract multicast groups' swiping probability distributions for accurate
resource demand prediction.
