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In this paper, we propose a digital twin (DT)-assisted cloud-edgecollaborative transcoding scheme to enhance user satisfaction in livestreaming. We first present a DT-assisted transcoding workload estimation (TWE)model for the cloud-edge collaborative transcoding. Particularly, two DTs areconstructed for emulating the cloud-edge collaborative transcoding process byanalyzing spatial-temporal information of individual videos and transcodingconfigurations of transcoding queues, respectively. Two light-weight Bayesianneural networks are adopted to fit the TWE models in DTs, respectively. We thenformulate a transcoding-path selection problem to maximize long-term usersatisfaction within an average service delay threshold, taking into account thedynamics of video arrivals and video requests. The problem is transformed intoa standard Markov decision process by using the Lyapunov optimization andsolved by a deep reinforcement learning algorithm. Simulation results based onthe real-world dataset demonstrate that the proposed scheme can effectivelyenhance user satisfaction compared with benchmark schemes.

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