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

Title	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.
Publication Date:	2023
Publication Place	- IEEE
Subject	AlphaRTC, Bandwidth, Bandwidth prediction, Bit rate, Estimation, Optimization, Prediction algorithms, Quality of experience, Real-time communications, Reinforcement learning, RTC, Streaming media, WebRTC
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1520-9210
Record ID	c0dcbaa5-4219-4bbe-a27c-086fd44b8ac0
Library Location	Computer Science
Date	2023
Sample Text	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

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

Publication Date 2023

Publication Place - IEEE

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1520-9210

Record ID c0dcbaa5-4219-4bbe-a27c-086fd44b8ac0

Library Location Computer Science

Date 2023

Sample Text 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