VideoNOC
Tarun Mangla, Ellen Witte Zegura, Mostafa Ammar, Emir Halepovic, Kyung-Wook Hwang, Rittwik Jana, Marco Platania · 2018
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).