无线多跳网络中以网络为中心的DASH

Mousie Fasil, Hussein Al-Shatri, Stefan Wilk, A. Klein
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引用次数: 1

摘要

由于终端用户设备的不同功能和不断变化的网络条件,无线多跳网络中的视频流是一个挑战。这一挑战在应用层通过自适应视频流方案解决,如HTTP上的动态自适应流(DASH),它被内容提供商广泛应用。DASH通过存储同一视频的几种表示来应对不同的终端用户设备功能,这样DASH就可以为用户提供多种质量的视频。然而,DASH中的调整仅发生在应用层。特别是在无线多跳网络中,底层的自适应尤为重要。因此,我们提出了一种新的应用感知跨层框架,该框架在网络层适应网络支持结构,在介质访问层进行资源分配,在物理层进行通信类型之间的切换,并在应用层考虑DASH的特性和要求。在此基础上,提出了一个综合考虑应用层、网络层、介质访问层和物理层的统一图模型。我们提出了一个二元线性问题,为每个用户选择最优的视频表示,并找到下三层机制的最佳组合,以通过无线多跳网络优化分发视频内容。我们表明,与不在不同机制之间切换的传统方法相比,我们利用转换的应用程序感知跨层框架的收益在15- 83%之间。
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A Network-Centric View on DASH in Wireless Multihop Networks
Video streaming in wireless multihop networks is a challenge due to different capabilities of end-user devices and changing network conditions. This challenge is addressed at the application layer with adaptive video streaming schemes like dynamic adaptive streaming over HTTP (DASH), which is widely applied by content providers. DASH copes with diverse end-user device capabilities by storing several representations of the same video such that DASH can offer a video in multiple qualities to users. Nevertheless, adjustments in DASH are solely taking place at the application layer. Especially in wireless multihop networks, adaptions on the lower layers are of particular importance. Therefore, we propose a novel application-aware cross-layer framework which adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the properties and requirements of DASH at the application layer. Furthermore, we present a unified graph model, which takes into account the application layer, the network layer, the medium access layer and the physical layer jointly. We formulate a binary linear problem which chooses the optimal video representation for each user and finds the best combination of mechanisms on the lower three layers to optimally distribute the video content through the wireless multihop network. We show that our application- aware cross-layer framework which utilizes transitions leads to gains between 15-83 % compared to conventional approaches that do not switch between different mechanisms.
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