  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.
