Abstract
Over the past decade, video streaming on the Internet has become the primary source of our media consumption. Billions of users stream online video on multiple devices with an increasing expectation that video will be delivered at high quality without any rebuffering or other events that affect their Quality of Experience (QoE). Video streaming platforms leverage Content Delivery Networks (CDNs) to achieve this at scale. However, there is a gap in how the quality of video streams is monitored. Current solutions rely on client-side beacons that are issued actively by video players. While such approaches may be feasible for streaming platforms that deploy their own CDN, they are less applicable for third-party CDNs with multiple tenants and diverse video players.
In this paper, we present a characterization of video workload from a global multi-tenant CDN and develop SSQoE: a methodology deployed on the server side which estimates rebuffering experienced by video clients using passive measurements. Using this approach, we calculate a QoE score which represents the health of a video stream across multiple consumers. We present our findings using this QoE score for various scenarios and compare it to traditional server and network monitoring metrics. We also demonstrate the QoE score’s efficacy during large streaming events such as the 2020 Superbowl LIV. We show that this server-side QoE estimation methodology is able to track video performance at an AS or user agent level and can easily pinpoint regional issues at the CDN, making it an attractive solution to be explored by researchers and other CDNs.
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Notes
- 1.
For visualization simplicity in the figures, each PoP is represented by the city/metro name it is located in.
- 2.
Network Operations Center (NOC) is responsible for 24\(\,\times \,\)7 monitoring of global CDN performance and respond to customer incidents.
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Shah, A., Bran, J., Zarifis, K., Bedi, H. (2022). SSQoE: Measuring Video QoE from the Server-Side at a Global Multi-tenant CDN. In: Hohlfeld, O., Moura, G., Pelsser, C. (eds) Passive and Active Measurement. PAM 2022. Lecture Notes in Computer Science, vol 13210. Springer, Cham. https://doi.org/10.1007/978-3-030-98785-5_27
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