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Congestion-aware edge caching for adaptive video streaming in Information-Centric Networks

机译:信息中心网络中用于自适应视频流的拥塞感知边缘缓存

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This paper proposes a network-aware resource management scheme that improves the quality of experience (QoE) for adaptive video streaming in CDNs and Information-Centric Networks (ICN) in general, and Dynamic Adaptive Streaming over HTTP (DASH) in particular. By utilizing the DASH manifest, the network (by way of a logically centralized controller) computes the available link resources and schedules the chunk dissemination to edge caches ahead of the end-user's requests. Our approach is optimized for multi-rate DASH videos. We implemented our resource management scheme, and demonstrated that in the scenario when network conditions evolve quickly, our approach can maintain smooth high quality playback. We show on actual video server data and in our own simulation environment that a significant reduction in peak bandwidth of 20% can be achieved using our approach.
机译:本文提出了一种网络感知资源管理方案,该方案可提高CDN和信息中心网络(ICN)中的自适应视频流,尤其是HTTP上的动态自适应流(DASH)的体验质量(QoE)。通过利用DASH清单,网络(通过逻辑上集中的控制器)计算可用的链接资源,并在最终用户的请求之前安排将块分发到边缘缓存的过程。我们的方法针对多速率DASH视频进行了优化。我们实施了资源管理方案,并证明了在网络条件快速变化的情况下,我们的方法可以保持流畅的高质量播放。我们在实际的视频服务器数据上以及在我们自己的仿真环境中显示,使用我们的方法可以将峰值带宽显着降低20%。

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