Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, 3G HSDPA data set from the Internet, as well as the 4G /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

中文翻译：

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超越 QoE：边缘视频流的多样性适应


自适应比特率 (ABR) 算法是高质量体验 (QoE) 互联网视频传输的关键技术。执行固定参数的整体 QoE 功能的早期 ABR 算法受到以下事实的限制：最终用户的 QoE 多种多样，因此视频比特率的选择常常具有误导性。 MPC 和 Pensieve 等最先进的 ABR 算法利用离线建模技术，导致在线 QoE 多样性自适应的性能下降。为了解决这个问题，我们提出了Elephanta，一种针对边缘用户的在线ABR算法，它结合了用户QoE感知接口和具有灵活参数的自适应算法。为了避免在线更新参数的开销，我们将视频流建模为更新系统，并通过对相应的 QoE 指标设置约束，将特定的 QoE 函数制定为灵活的格式。为了验证参数设置，我们在 1500 个吞吐量轨迹下模拟 Elephanta，包括 FCC 宽带、来自互联网的 3G HSDPA 数据集以及我们收集的 4G /LTE 数据集。评估结果表明，在 QoE 多样性下，Elephanta 比 MPC 提高了 7%，比 Pensieve 提高了 3%，部分原因在于它对 QoE 多样性具有卓越的适应性。我们在客户端的 dash.js 中实现了 Elephanta 来进行主观实验。我们观察到用户之间存在不同的 QoE 偏好，19/21 用户（强烈）同意 Elephanta 在观看视频时对参数变化做出响应。