Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts
Too Late for Playback: Estimation of Video Stream Quality in Rural and Urban Contexts
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DOI：
10.1007/978-3-030-72582-2_9
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发表时间：
2021
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通讯作者：
V. Adarsh;Michael Nekrasov;Udit Paul;Alexander Ermakov;Arpit Gupta;Morgan Vigil-Hayes;E. Zegura;E. Belding-Royer
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作者：
V. Adarsh;Michael Nekrasov;Udit Paul;Alexander Ermakov;Arpit Gupta;Morgan Vigil-Hayes;E. Zegura;E. Belding-Royer
移动宽带作为互联网连接的基本手段的爆炸性增长，使得对通过LTE网络传输的应用的体验质量(QOE)进行可扩展的评估和推断变得至关重要。然而，直接的QOE测量可能会耗费大量的时间和资源。此外，LTE网络的无线特性要求必须在每个基站的多个位置评估QOE，因为诸如信号可用性的因素可能具有显著的空间变化。基于我们观察到的服务质量(Qos)度量收集的时间和资源密集度较少，我们研究了如何使用Qos来推断LTE网络中的QOE。使用一个代表各种网络条件的广泛的、新颖的数据集，我们设计了几个最先进的预测模型，用于可伸缩的视频QOE推理。我们证明，我们的模型可以在80%以上的时间内准确地预测再缓冲事件和分辨率切换，尽管数据集对于不同的位置类型表现出非常不同的QOS和QOE配置文件。我们还说明，我们的分类器对来自大量流派的多个视频具有高度的泛化能力。最后，我们通过消融研究强调了参考信号接收功率(RSRP)和吞吐量等低成本的QOS测量在QOE推断中的重要性。
The explosion of mobile broadband as an essential means of Internet connectivity has made the scalable evaluation and inference of quality of experience (QoE) for applications delivered over LTE networks critical. However, direct QoE measurement can be time and resource intensive. Further, the wireless nature of LTE networks necessitates that QoE be evaluated in multiple locations per base station as factors such as signal availability may have significant spatial variation. Based on our observations that quality of service (QoS) metrics are less time and resource-intensive to collect, we investigate how QoS can be used to infer QoE in LTE networks. Using an extensive, novel dataset representing a variety of network conditions, we design several state-of-the-art predictive models for scalable video QoE inference. We demonstrate that our models can accurately predict rebuffering events and resolution switching more than 80% of the time, despite the dataset exhibiting vastly different QoS and QoE profiles for the location types. We also illustrate that our classifiers have a high degree of generalizability across multiple videos from a vast array of genres. Finally, we highlight the importance of low-cost QoS measurements such as reference signal received power (RSRP) and throughput in QoE inference through an ablation study.