BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning

Name: BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning
Author: Bentaleb, A., Akçay, Mehmet Necmettin, Lim, M., Beğen, Ali Cengiz, Zimmermann, R.

İsim	BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning
Yazar	Bentaleb, A., Akçay, Mehmet Necmettin, Lim, M., Beğen, Ali Cengiz, Zimmermann, R.
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	AlphaRTC, Bandwidth, Bandwidth prediction, Bit rate, Estimation, Optimization, Prediction algorithms, Quality of experience, Real-time communications, Reinforcement learning, RTC, Streaming media, WebRTC
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1520-9210
Kayıt Numarası	c0dcbaa5-4219-4bbe-a27c-086fd44b8ac0
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	Bandwidth 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.
DOI	10.1109/TMM.2022.3216456
Cilt	25

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BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning

Yazar Bentaleb, A., Akçay, Mehmet Necmettin, Lim, M., Beğen, Ali Cengiz, Zimmermann, R.

Basım Tarihi 2023

Basım Yeri - IEEE

Konu AlphaRTC, Bandwidth, Bandwidth prediction, Bit rate, Estimation, Optimization, Prediction algorithms, Quality of experience, Real-time communications, Reinforcement learning, RTC, Streaming media, WebRTC

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1520-9210

Kayıt Numarası c0dcbaa5-4219-4bbe-a27c-086fd44b8ac0

Lokasyon Computer Science

Tarih 2023

Örnek Metin Bandwidth 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.

DOI 10.1109/TMM.2022.3216456

Cilt 25