Bentaleb, A.Akçay, Mehmet NecmettinLim, M.Beğen, Ali CengizZimmermann, R.2023-08-092023-08-0920231520-9210http://hdl.handle.net/10679/8614https://doi.org/10.1109/TMM.2022.3216456Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts the bitrate to achieve the best quality without congesting the network and causing packets to be lost or delayed. To date, several RTC services have deployed the heuristic-based Google Congestion Control (GCC), which performs well under certain circumstances and falls short in some others. In this paper, we leverage the advancements in reinforcement learning and propose BoB (Bang-on-Bandwidth) — a hybrid bandwidth predictor for RTC. At the beginning of the RTC session, BoB uses a heuristic-based approach. It then switches to a learning-based approach. BoB predicts the available bandwidth accurately and improves bandwidth utilization under diverse network conditions compared to the two winning solutions of the ACM MMSys'21 grand challenge on bandwidth estimation in RTC. An open-source implementation of BoB is publicly available for further testing and research.engAttribution 4.0 InternationalopenAccesshttp://creativecommons.org/licenses/by/4.0/article256930694500113327830000310.1109/TMM.2022.3216456AlphaRTCBandwidthBandwidth predictionBit rateEstimationOptimizationPrediction algorithmsQuality of experienceReal-time communicationsReinforcement learningRTCStreaming mediaWebRTC2-s2.0-85140709165