Offline reinforcement learning for bandwidth estimation in rtc using a fast actor and not-so-furious critic

Name: Offline reinforcement learning for bandwidth estimation in rtc using a fast actor and not-so-furious critic
Author: Begen, Ali C., Deniz, Enes, Erinc, Yigit K., Ozgun, Mehmet E., Yumakogullari, Basar, Ayten, Ihsan U., Pehlivanoglu, Ahmet, Cetinkaya, Ekrem

İsim	Offline reinforcement learning for bandwidth estimation in rtc using a fast actor and not-so-furious critic
Yazar	Begen, Ali C., Deniz, Enes, Erinc, Yigit K., Ozgun, Mehmet E., Yumakogullari, Basar, Ayten, Ihsan U., Pehlivanoglu, Ahmet, Cetinkaya, Ekrem
Basım Tarihi:	2024-04-17
Basım Yeri	- ACM
Konu	WebRTC, QoE, Real-time communication
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-8-4007-0412-3
Kayıt Numarası	a006fcbd-475f-44ca-8341-5b74237e8963
Lokasyon	Computer Science
Tarih	2024-04-17
Örnek Metin	The increasing demand for real-time communication (RTC) applications necessitates robust and reliable systems. Seamless media delivery depends on an accurate assessment of the network conditions, with bandwidth estimation (BWE) being crucial for maintaining system reliability and achieving good quality of experience (QoE) for the users. BWE poses a significant challenge due to dynamic network conditions, limited information availability and computational complexity. The Second Bandwidth Estimation Challenge, organized within ACM MMSys 2024, aims to enhance RTC user QoE by developing a deep learning-based bandwidth estimator using offline reinforcement learning. This paper presents our solution, ranked second in the grand challenge. This solution employs an actor-critic approach to achieve accurate real-time BWE by relying solely on observed network statistics. Due to the offline setting of the challenge, the critic network is trained separately from the actor network to estimate the action quality without interacting with the real environment. Furthermore, the quality prediction by the critic is adjusted by a predefined conservation factor to address overshooting the bandwidth values. The solution's source code is publicly available at https://github.com/streaminguniversity/FARC.
DOI	10.1145/3625468.3652184

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Offline reinforcement learning for bandwidth estimation in rtc using a fast actor and not-so-furious critic

Yazar Begen, Ali C., Deniz, Enes, Erinc, Yigit K., Ozgun, Mehmet E., Yumakogullari, Basar, Ayten, Ihsan U., Pehlivanoglu, Ahmet, Cetinkaya, Ekrem

Basım Tarihi 2024-04-17

Basım Yeri - ACM

Konu WebRTC, QoE, Real-time communication

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-8-4007-0412-3

Kayıt Numarası a006fcbd-475f-44ca-8341-5b74237e8963

Lokasyon Computer Science

Tarih 2024-04-17

Örnek Metin The increasing demand for real-time communication (RTC) applications necessitates robust and reliable systems. Seamless media delivery depends on an accurate assessment of the network conditions, with bandwidth estimation (BWE) being crucial for maintaining system reliability and achieving good quality of experience (QoE) for the users. BWE poses a significant challenge due to dynamic network conditions, limited information availability and computational complexity. The Second Bandwidth Estimation Challenge, organized within ACM MMSys 2024, aims to enhance RTC user QoE by developing a deep learning-based bandwidth estimator using offline reinforcement learning. This paper presents our solution, ranked second in the grand challenge. This solution employs an actor-critic approach to achieve accurate real-time BWE by relying solely on observed network statistics. Due to the offline setting of the challenge, the critic network is trained separately from the actor network to estimate the action quality without interacting with the real environment. Furthermore, the quality prediction by the critic is adjusted by a predefined conservation factor to address overshooting the bandwidth values. The solution's source code is publicly available at https://github.com/streaminguniversity/FARC.

DOI 10.1145/3625468.3652184