Data-driven bandwidth prediction models and automated model selection for low latency

Name: Data-driven bandwidth prediction models and automated model selection for low latency
Author: Bentaleb, A., Beğen, Ali Cengiz, Harous, S., Zimmermann, R.

İsim	Data-driven bandwidth prediction models and automated model selection for low latency
Yazar	Bentaleb, A., Beğen, Ali Cengiz, Harous, S., Zimmermann, R.
Basım Tarihi:	2021
Basım Yeri	- IEEE
Konu	ABR, Bandwidth prediction, Chunked transfer encoding, CMAF, DASH, HTTP adaptive streaming, Low latency
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1520-9210
Kayıt Numarası	9aaecf4c-1bc3-4d85-88c3-12a3e8592da4
Lokasyon	Computer Science
Tarih	2021
Notlar	Ministry of Education, Singapore ; UAE University
Örnek Metin	Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.
DOI	10.1109/TMM.2020.3013387
Cilt	23

Kaynağa git Özyeğin Üniversitesi

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Özyeğin Üniversitesi

Kaynağa git

Data-driven bandwidth prediction models and automated model selection for low latency

Yazar Bentaleb, A., Beğen, Ali Cengiz, Harous, S., Zimmermann, R.

Basım Tarihi 2021

Basım Yeri - IEEE

Konu ABR, Bandwidth prediction, Chunked transfer encoding, CMAF, DASH, HTTP adaptive streaming, Low latency

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1520-9210

Kayıt Numarası 9aaecf4c-1bc3-4d85-88c3-12a3e8592da4

Lokasyon Computer Science

Tarih 2021

Notlar Ministry of Education, Singapore ; UAE University

Örnek Metin Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.

DOI 10.1109/TMM.2020.3013387

Cilt 23