A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the...

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Autores principales:	Obinna Izima, Ruairí de Fréin, Ali Malik
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning quality of delivery video streaming Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/174881dd7bd34550a1970124d3888660
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spelling	oai:doaj.org-article:174881dd7bd34550a1970124d38886602021-11-25T17:25:19ZA Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics10.3390/electronics102228512079-9292https://doaj.org/article/174881dd7bd34550a1970124d38886602021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2851https://doaj.org/toc/2079-9292A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.Obinna IzimaRuairí de FréinAli MalikMDPI AGarticlemachine learningquality of deliveryvideo streamingElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2851, p 2851 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning quality of delivery video streaming Electronics TK7800-8360
spellingShingle	machine learning quality of delivery video streaming Electronics TK7800-8360 Obinna Izima Ruairí de Fréin Ali Malik A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
description	A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.
format	article
author	Obinna Izima Ruairí de Fréin Ali Malik
author_facet	Obinna Izima Ruairí de Fréin Ali Malik
author_sort	Obinna Izima
title	A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
title_short	A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
title_full	A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
title_fullStr	A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
title_full_unstemmed	A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
title_sort	survey of machine learning techniques for video quality prediction from quality of delivery metrics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/174881dd7bd34550a1970124d3888660
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A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

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