VideoNOC: assessing video QoE for network operators using passive measurements
VideoNOC: assessing video QoE for network operators using passive measurements
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VideoNOC：使用无源测量评估网络运营商的视频 QoE
DOI：
10.1145/3204949.3204956
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Platania
中科院分区：
文献类型：
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作者：
Tarun Mangla;E. Zegura;M. Ammar;Emir Halepovic;Kyung;R. Jana;M. Platania
移动网络中的视频流流量正在迅速增长。移动网络运营商（mno）有望跟上这一不断增长的需求，同时保持高视频体验质量（QoE）。这使得mno对用户的视频QoE有一个深入的了解，以帮助进行网络规划、配置和流量管理变得至关重要。然而，设计一个测量视频QoE的系统面临着几个挑战：i)大规模的视频流量数据和视频流服务的多样性，ii)由于复杂的蜂窝网络架构而产生的跨层约束，以及iii)从网络流量中提取QoE指标。在本文中，我们提出了VideoNOC，这是一个灵活且可扩展的平台原型，用于推断mno的客观视频QoE指标（例如，比特率，再缓冲）。我们描述了VideoNOC的设计和体系结构，并概述了为细粒度视频QoE监控生成新数据源的方法。然后，我们将演示这种监视系统的一些用例。VideoNOC揭示了整个网络的视频需求，为内容提供商的许多设计选择提供了有价值的见解（例如，操作系统相关的性能，视频播放器参数，如缓冲区大小，编码比特率范围等），并帮助分析网络条件对视频QoE的影响（例如，移动性和高需求）。
Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).