HTTP adaptive streaming (HAS) technology has been increasingly employed by video service providers (VSPs) due to its prominent benefits such as reducing interruptions of video playback and achieving higher bandwidth utilization and outstanding quality of experience (QoE). And many VSPs have deployed HAS applications in the media cloud to provide large-scale video streaming services. At present, research into the media cloud typically focuses on the management and optimization of cloud resources, such as the placement and migration of virtual machines in media cloud data centers. However, considering the HAS live video streaming service, existing related works have not adequately discussed the specific impact of the consumption of computing and bandwidth resources of media cloud servers on the user experience (QoE), particularly under the resource constraints in the media cloud. In this paper, we first investigate and formulate the computing and bandwidth resource consumption characteristics of HAS live video streaming with different frame rates and resolutions, and we further establish a resources-aware QoE model to quantify the user experience of live video channels (i.e., programs). Then, based on the model, we present a QoE-driven HAS live video channel placement approach (including a placement algorithm HCP and a rescheduling algorithm HCR ) to optimize the channel allocation in media cloud servers, aiming to maximize the average user QoE. We abstract the maximization problem into an MMKP problem, and employ a heuristic solution to address this problem. The experimental results demonstrate the effectiveness of our proposed approach compared with benchmark solutions.

中文翻译：

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QoE驱动的HAS实时视频通道在媒体云中的位置

HTTP 自适应流媒体 (HAS) 技术因其突出的优势而越来越多地被视频服务提供商 (VSP) 使用，例如减少视频播放中断、实现更高的带宽利用率和出色的体验质量 (QoE)。并且很多VSP已经在媒体云中部署了HAS应用来提供大规模的视频流服务。目前，媒体云的研究主要集中在云资源的管理和优化，例如媒体云数据中心虚拟机的放置和迁移。但是，考虑到HAS直播视频流服务，现有的相关工作并没有充分讨论媒体云服务器的计算和带宽资源消耗对用户体验（QoE）的具体影响，特别是在媒体云资源限制下。在本文中，我们首先研究并制定了具有不同帧速率和分辨率的HAS实时视频流的计算和带宽资源消耗特征，然后我们进一步建立了资源感知的QoE模型来量化用户体验。直播视频频道（即程序）。然后，基于该模型，我们提出了一种 QoE 驱动的 HAS 直播视频频道放置方法（包括放置算法保健品 和重新调度算法 HCR ) 优化媒体云服务器中的频道分配，旨在最大化平均用户 QoE。我们将最大化问题抽象为 MMKP 问题，并采用启发式解决方案来解决此问题。与基准解决方案相比，实验结果证明了我们提出的方法的有效性。