Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming
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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.
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