By the envision of combing smooth viewing experience with high-efficiency content distribution, dynamic adaptive streaming (DAS) over information-centric networking (ICN) is becoming a promising trend for the future video services. However, optimizations of DAS flow transmission control and rate adaptation need to be revisited for better adopting the ICN with multicast, multi-rate forwarding and decentralized framework. In this paper, we propose a decentralized asynchronous method for ICN-DAS. We first formulate the problem as a two-stage optimization, wherein the first stage's objective is to optimize the transmission rate within network capacity constraints, and the second is adapting the video bitrate for the long-term viewing utility. A distributed asynchronous optimization algorithm (DAOA) is then proposed for solving the two-stage problem iteratively by a novel distributed switching mirror descent and virtual queue-based iterations. Analytic results including convergence, computation complexity and time-varying adaptation are provided to validate theoretically the DAOA's performance. Simulation-based testing has also been conducted for evaluating DAOA's performance and assess its viewing experience, in comparison with state-of-the-art solutions.

通过将流畅的观看体验与高效的内容分发相结合的设想，基于信息中心网络（ICN）的动态自适应流（DAS）成为了未来视频服务的有希望的趋势。但是，为了更好地采用具有多播，多速率转发和分散框架的ICN，需要重新审视DAS流传输控制和速率适配的优化。在本文中，我们提出了一种用于ICN-DAS的分散式异步方法。我们首先将问题表述为两阶段优化，其中第一阶段的目标是在网络容量限制内优化传输速率，第二阶段的目的是使视频比特率适应长期观看的效用。然后提出了一种分布式异步优化算法（DAOA），通过一种新颖的分布式交换镜像下降和基于虚拟队列的迭代来迭代地解决两阶段问题。提供了包括收敛，计算复杂度和时变适应性在内的分析结果，以从理论上验证DAOA的性能。与最新解决方案相比，还进行了基于仿真的测试，以评估DAOA的性能并评估其观看体验。