作为一种可能的未来网络架构，信息中心网络（ICN）已经成为研究热点，其中命名数据网络（NDN）是影响最大的ICN实例，目前大多数ICN相关研究都在NDN架构上开展。但是NDN在协议架构和性能优化方面还存在较多问题，本项目引入“流”的思想，设计更加高效的基于“流”的NDN框架（f-NDN），在该框架基础上建立f-NDN转发过程的Markov决策过程模型，并采用基于灵敏度的优化方法计算最优转发策略。在新网络架构基础上，采用大偏差理论研究f-NDN架构中视频流可用带宽估计算法，采用模型预测控制实现可伸缩视频自适应传输控制方法。通过开发实验验证平台验证f-NDN协议和视频流自适应传输控制算法的有效性。本项目希望通过设计新型的网络架构方案，以及将控制理论和优化方法引入到网络及其业务系统性能优化，为我国未来网络的研究和建设提供可靠的理论依据与技术支撑。

As a candidate of future Internet architecture, information center network (ICN) has become a hot topic. Named Data Network (NDN) is the most influential ICN instance, and most ICN researches are carried out on the NDN architecture. However, there are still some problems in the NDN architecture and its‘ performance optimization. In this proposal, we plan to introduce the idea of "flow" into NDN, and design the more efficient flow-based NDN architecture (f-NDN) aiming at breaking the existing NDN key technical weakness and bottlenecks. Then, we established f-NDN forwarding process Markov decision process model, and obtain optimal forwarding strategy based on sensitivity analysis. Based on f-NDN, we research the video adaptive transmission and control by large deviation theory and model predictive control (MPC). Finally, prototype of f-NDN is developed which can verify the performance of protocol and algorithm. The project designs the new network architecture solutions, as well as introduces the control theory and optimization method into the network performance optimization, aiming at providing a reliable theoretical basis and technical support for future Internet research.