Hindsight
Te-Yuan Huang, Chaitanya Ekanadham, Andrew J. Berglund, Zhi Li · 2019
The Adaptive bitrate algorithm (ABR) is an essential part of any HTTP-based video streaming service. Given the endless array of network environments, device capabilities, and content properties in a commercial setting, perfecting ABR remains challenging. To identify shortcomings effectively at a large scale, a scalable methodology is needed to evaluate ABR algorithms under various scenarios. The state-of-the-art method is to evaluate a production ABR retrospectively with an optimal ABR algorithm. However, optimal ABR is an NP-hard problem and therefore is costly to be deployed at a commercial scale. As a result, shortcomings in the field are often identified through manual inspection. The process is labor-intensive and often relies on experience and intuitions built from reviewing the characteristics of a large number of sessions. Motivated by our operational experience, in this paper we propose an efficient approximation for the optimal ABR problem, thus enabling large-scale deployment and benchmarking of production ABR algorithms.