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

TL;DR

This paper introduces a digital twin-assisted cloud-edge collaborative transcoding scheme that uses Bayesian neural networks and deep reinforcement learning to optimize transcoding paths, significantly improving user satisfaction in live streaming.

Contribution

It presents a novel DT-assisted workload estimation model and a deep RL-based path selection method for live streaming transcoding, enhancing user satisfaction.

Findings

Effective improvement in user satisfaction over benchmarks

Accurate workload estimation via Bayesian neural networks

Optimized transcoding path selection under delay constraints

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.