Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and objective QoE prediction models. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. The new database focuses on low bandwidth conditions which are more challenging for bitrate adaptation algorithms, which often must navigate tradeoffs between rebuffering and video quality. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models and analyze their strengths and weaknesses. We believe that the tools introduced here will help inspire further progress on the development of perceptually-optimized client adaptation and video streaming strategies. The database is publicly available at http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html .

中文翻译：

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走向感知优化的自适应视频流 - 一个现实的体验质量数据库

测量体验质量 (QoE) 并将这些测量结果集成到视频流算法中是一个多方面的问题，从根本上需要设计综合的主观 QoE 数据库和客观 QoE 预测模型。为了实现这一目标，我们最近设计了 LIVE-NFLX-II 数据库，这是一个高度逼真的数据库，其中包含对各种设计维度的主观 QoE 响应，例如比特率自适应算法、网络条件和视频内容。我们的数据库建立在内容自适应编码的最新进展之上，并结合了实际的网络跟踪来捕获客户端设备上的真实网络变化。新数据库侧重于对比特率自适应算法更具挑战性的低带宽条件，这通常必须在重新缓冲和视频质量之间进行权衡。使用我们的数据库，我们研究了多个流维度对用户体验的影响，评估视频质量和体验模型的质量并分析它们的优缺点。我们相信这里介绍的工具将有助于激发开发的进一步进展感知优化客户端适配和视频流策略。该数据库公开在http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html .