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arXiv · 2211.06906

Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming

Xinyu Huang·Mushu Li·Wen Wu·Conghao Zhou·Xuemin Sherman Shen
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Abstract

In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) model for the cloud-edge collaborative transcoding. Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively. Two light-weight Bayesian neural networks are adopted to fit the TWE models in DTs, respectively. We then formulate a transcoding-path selection problem to maximize long-term user satisfaction within an average service delay threshold, taking into account the dynamics of video arrivals and video requests. The problem is transformed into a standard Markov decision process by using the Lyapunov optimization and solved by a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed scheme can effectively enhance user satisfaction compared with benchmark schemes.

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Xinyu Huang, Mushu Li, Wen Wu, Conghao Zhou, Xuemin Sherman Shen. 2022-11-13. Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming. https://arxiv.org/abs/2211.06906

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