Digital twin (DT) is revolutionizing the emerging video streaming services through tailored network management. By integrating diverse advanced communication technologies, DTs are promised to construct a holistic virtualized network for better network management performance. To this end, we develop a DT-driven network architecture for video streaming (DTN4VS) to enable network virtualization and tailored network management. With the architecture, various types of DTs can characterize physical entities' status, separate the network management functions from the network controller, and empower the functions with emulated data and tailored strategies. To further enhance network management performance, three potential approaches are proposed, i.e., domain data exploitation, performance evaluation, and adaptive DT model update. We present a case study pertaining to DT-assisted network slicing for short video streaming, followed by some open research issues for DTN4VS.

翻译：数字孪生正通过定制化网络管理革新新兴的视频流服务。通过整合多种先进通信技术，数字孪生有望构建全面的虚拟化网络，以提升网络管理性能。为此，我们开发了一种面向视频流的数字孪生驱动网络架构（DTN4VS），实现网络虚拟化与定制化网络管理。在该架构下，各类数字孪生体可表征物理实体的状态，将网络管理功能从网络控制器中分离，并通过仿真数据与定制化策略赋能这些功能。为进一步优化网络管理性能，本文提出三种潜在方法，即领域数据挖掘、性能评估与自适应数字孪生模型更新。我们通过数字孪生辅助短时视频流的网络切片案例研究，展示了该架构的可行性，并探讨了DTN4VS面临的部分开放性研究问题。