Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

中文翻译：

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移动QoE监控DASH视频流：模型和多个移动客户端的研究

近年来，基于HTTP的动态自适应流（DASH）视频流的客观体验质量（QoE）受到了广泛的关注。虽然有许多客观的QoE模型，但是当前模型的局限性在于，在交付整个视频后才提供QoE；同样，这些模型是基于每个客户的。对于内容服务提供商，观察到的QoE对于监控以了解流传输期间的整体性能非常重要，例如实时事件或多个客户端流传输时的并发流传输。为此，我们提出了移动QoE（简称MQoE）模型来在多个同时客户端的视频流期间定期测量QoE。我们的第一个模型MQoE_RF是一个非线性模型，考虑了比特率增益和来自比特率切换频率的灵敏度。我们的第二个模型MQoE_SD是一个线性模型，其重点是捕获段之间的比特率切换幅度的标准偏差以及比特率增益。然后，我们研究两种模型在多用户移动客户端环境中的有效性，其移动性模式基于火车，汽车或轮渡的轨迹。我们在GENI测试平台上实施了这项研究。我们的研究表明，与静态QoE模型相比，我们的MQoE模型在捕获传输过程中的QoE行为方面更为准确。此外，我们的MQoE_RF模型可以更有效地捕获由于比特率切换频率引起的灵敏度，而MQoE_SD模型则可以捕获由于比特率切换幅度导致的灵敏度。这两种模型都适合内容服务提供商根据其偏好来监视视频流。