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QoE-Driven Rate Adaptation Algorithm for Fair Dynamic Adaptive Video Streaming in Named Data Networking

机译：命名数据网络中公平动态自适应视频流的QoE驱动率适应算法

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摘要

Nowadays, video streaming causes traffic over the Internet. Dynamic Adaptive video Streaming (DAS) has emerged as a promising solution. However, existing researches in DAS focus on single client instead of multi-client which is much closer to reality. Even if some methods are proposed for multi-client scenarios, they need to add coordinate proxy to the network or add additional function to routers or servers. Besides, the caches in Named Data Networking (NDN) have a significant impact on clients. Consequently, we proposed a QoE-driven fair-DAS algorithm, able to learn and dynamically adapt its behavior depending on network conditions. By evaluating this novel approach through simulations in several multi-client scenarios and comparing with the Rate and Buffer Based Adaptation (RBBA) algorithm, we are able to show that our algorithm resulted in a better video quality, fairness, stability and effectiveness, so a great improvement in overall QoE.
机译：如今，视频流导致互联网的流量。动态自适应视频流（DAS）已成为有希望的解决方案。然而，DAS的现有研究专注于单个客户而不是多客户，这与现实更近。即使提出了用于多客户方案的某些方法，它们也需要将坐标代理添加到网络上，或向路由器或服务器添加其他函数。此外，命名数据网络（NDN）的高速缓存对客户产生了重大影响。因此，我们提出了一种QoE驱动的公平DAS算法，能够根据网络条件学习和动态地调整其行为。通过在几个多客户情景中的模拟中评估这种新方法并与基于速率和缓冲的适应（RBBA）算法进行比较，我们能够表明我们的算法导致更好的视频质量，公平，稳定性和有效性，因此整体QoE的巨大改善。

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