Oboe: auto-tuning video ABR algorithms to network conditions

By Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay G. Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, Hui Zhang. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2018

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Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing (2019, methodology): “The representation of the bandwidth states is motivated by the existing study of adaptive video streaming [43], where the throughput of TCP connection can be modeled as a piece-wise stationary process where the connection consists of multiple non-overlapping stationary segments.”
A variegated look at 5G in the wild: performance, power, and QoE implications (2021, background): “There have also been efforts on evaluating the performance of different ABR algorithms for HTTP adaptive streaming [21, 37, 38, 61].”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, methodology): “[131] designed Oboe which allows the automatic tuning of configuration parameters to different network conditions for an ABR scheme.”
Learning in situ: a randomized experiment in video streaming (2019, background): “In the academic literature, many recent ABR algorithms use statistical and machine-learning methods [2,23,35,36,38,43], which allow algorithms to consider many input signals and try to perform well for a wide variety of clients.”