论文标题
数字双胞胎辅助协作转码，以在实时流中获得更好的用户满意度
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
论文作者
论文摘要
在本文中，我们提出了一个数字双胞胎（DT）辅助的云边缘协作转码方案，以增强实时流中的用户满意度。我们首先提出了DT辅助转码工作负载估计（TWE）模型，用于云边缘协作转码。特别是，构建了两个DT，用于通过分别分析单个视频的空间信息和转码转换队列的配置来模拟云边缘协作转码过程。采用了两个轻巧的贝叶斯神经网络，以分别适合DTS中的TWE模型。然后，我们制定一个转码路径选择问题，以考虑到视频到达和视频请求的动态，以在平均服务延迟阈值中最大化长期用户满意度。该问题通过使用Lyapunov优化并通过深入的增强学习算法解决，将问题转化为标准的马尔可夫决策过程。基于现实世界数据集的仿真结果表明，与基准方案相比，所提出的方案可以有效提高用户满意度。
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.
