Meta reinforcement learning for rate adaptation

Name: Meta reinforcement learning for rate adaptation
Author: Bentaleb, A., Lim, M., Akçay, Mehmet Necmettin, Beğen, Ali Cengiz, Zimmermann, R.

İsim	Meta reinforcement learning for rate adaptation
Yazar	Bentaleb, A., Lim, M., Akçay, Mehmet Necmettin, Beğen, Ali Cengiz, Zimmermann, R.
Basım Tarihi:	2023
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835033414-2
Kayıt Numarası	7c033c0f-c3d5-4bfa-984d-45d0c1d5a8a3
Lokasyon	Computer Science
Tarih	2023
Notlar	TÜBİTAK
Örnek Metin	Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions to achieve a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. Heuristics are easy to implement but do not always perform well, whereas learning-based methods generally perform well but are difficult to deploy on low-resource devices. To make the most out of both worlds, we develop Ahaggar, a learning-based scheme running on the server side that provides quality-aware bitrate guidance to streaming clients running their own heuristics. Ahaggar's novelty is the meta reinforcement learning approach taking network conditions, clients' statuses and device resolutions, and streamed content as input features to perform bitrate guidance. Ahaggar uses the new Common Media Client/Server Data (CMCD/SD) protocols to exchange the necessary metadata between the servers and clients. Experiments on an open-source system show that Ahaggar adapts to unseen conditions fast and outperforms its competitors in several viewer experience metrics.
DOI	10.1109/INFOCOM53939.2023.10228951

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Meta reinforcement learning for rate adaptation

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

Basım Tarihi 2023

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835033414-2

Kayıt Numarası 7c033c0f-c3d5-4bfa-984d-45d0c1d5a8a3

Lokasyon Computer Science

Tarih 2023

Notlar TÜBİTAK

Örnek Metin Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions to achieve a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. Heuristics are easy to implement but do not always perform well, whereas learning-based methods generally perform well but are difficult to deploy on low-resource devices. To make the most out of both worlds, we develop Ahaggar, a learning-based scheme running on the server side that provides quality-aware bitrate guidance to streaming clients running their own heuristics. Ahaggar's novelty is the meta reinforcement learning approach taking network conditions, clients' statuses and device resolutions, and streamed content as input features to perform bitrate guidance. Ahaggar uses the new Common Media Client/Server Data (CMCD/SD) protocols to exchange the necessary metadata between the servers and clients. Experiments on an open-source system show that Ahaggar adapts to unseen conditions fast and outperforms its competitors in several viewer experience metrics.

DOI 10.1109/INFOCOM53939.2023.10228951