SVC-Based Multi-User Streamloading for Wireless Networks
SVC-Based Multi-User Streamloading for Wireless Networks
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DOI：
10.1109/jsac.2016.2577218
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发表时间：
2015-12
影响因子：
16.4
通讯作者：
S. A. Hosseini;Zheng Lu;G. Veciana;S. Panwar
中科院分区：
文献类型：
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作者：
S. A. Hosseini;Zheng Lu;G. Veciana;S. Panwar
在本文中，我们提出了一种方法，联合速率分配和质量选择的一种新的视频流方案称为流加载。流式加载是最近开发的用于在不违反对视频流的内容访问的版权强制限制的情况下递送高质量视频的方法。在常规流媒体服务中，内容提供商限制用户在回放之前可以下载的可观看视频的量。这种方法可能由于带宽变化而导致较差的用户体验，特别是在具有变化容量的移动的网络中。在流式加载中，视频使用可伸缩视频编码进行编码，并且允许用户预取增强层并将其存储在设备上，而基本层以近实时的方式进行流式传输，以确保满足对可视内容的缓冲约束。我们开始通过制定联合优化无线网络中的流加载的速率分配和质量选择的离线问题。这促使我们提出的联合调度在基站和段质量选择在接收机的在线算法。结果表明，流加载优于国家的最先进的流方案方面的额外的流，我们可以承认一个给定的视频质量。此外，我们提出的算法的质量自适应机制实现了比基线算法更高的性能，其中没有（或有限）以视频为中心的基站资源分配优化，例如，比例公平。
In this paper, we present an approach for joint rate allocation and quality selection for a novel video streaming scheme called streamloading. Streamloading is a recently developed method for delivering high-quality video without violating copyright enforced restrictions on content access for video streaming. In regular streaming services, content providers restrict the amount of viewable video that users can download prior to playback. This approach can cause inferior user experience due to bandwidth variations, especially in mobile networks with varying capacity. In streamloading, the video is encoded using scalable video coding, and users are allowed to pre-fetch enhancement layers and store them on the device, while base layers are streamed in a near real-time fashion ensuring that buffering constraints on viewable content are met. We begin by formulating the offline problem of jointly optimizing rate allocation and quality selection for streamloading in a wireless network. This motivates our proposed online algorithms for joint scheduling at the base station and segment quality selection at receivers. The results indicate that streamloading outperforms the state-of-the-art streaming schemes in terms of the number of additional streams we can admit for a given video quality. Furthermore, the quality adaptation mechanism of our proposed algorithm achieves a higher performance than baseline algorithms with no (or limited) video-centric optimization of the base station's allocation of resources, e.g., proportional fairness.