Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users’ quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The proposed metric prefers to use bandwidth of the links to optimize a concave utility function of the chunk quality. Even though the formulation is a non-convex discrete optimization, we provide a quadratic complexity algorithm which is shown to be optimal in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.

中文翻译：

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具有可扩展视频编码的优化偏好感知多路径视频流

大多数客户端主机都配备了多个网络接口（例如，WiFi 和蜂窝网络）。同时访问多个接口可以显着提高用户在视频流中的体验质量（QoE）。实现它的直观方法是使用多路径 TCP (MPTCP)。然而，MPTCP的部署，尤其是链路优先，需要客户端和服务器端的OS内核更新，大量的商业内容提供商不支持MPTCP。因此，在本文中，我们通过考虑可扩展视频编码 (SVC) 在应用层实现了一种多路径视频流算法，其中每个块的每一层只能从一个正交路径中获取。我们根据不同路径的可用带宽、块期限、和链接偏好作为一个优化问题。目标是优化 QoE 指标，在最大化每个块的播放速率和确保块之间的公平性之间保持权衡。所提出的度量更倾向于使用链接的带宽来优化块质量的凹效用函数。即使公式是非凸离散优化，我们也提供了二次复杂度算法，该算法在某些特殊情况下是最佳的。我们进一步提出了一种在线算法，其中解决了包括带宽预测错误在内的几个挑战。在具有公共数据集真实痕迹的真实测试台中进行的大量模拟实验揭示了我们方案的稳健性，并证明了与其他多路径算法相比，其显着的性能改进。