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（DT）正通过定制化网络管理革新新兴视频流服务。通过集成多种先进通信技术，数字孪生有望构建整体虚拟化网络，从而提升网络管理性能。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），以实现网络虚拟化与定制化网络管理。借助该架构，多种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，并利用仿真数据与定制策略赋能这些功能。为进一步提升网络管理性能，我们提出了三种潜在方法，即领域数据挖掘、性能评估及自适应数字孪生模型更新。我们通过一项关于数字孪生辅助短视频流网络切片的案例研究，展示了该架构的实际应用，并探讨了DTN4VS面临的若干开放性研究问题。