QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning

With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the...

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Autores principales:	Jie Liu, Xiaoming Tao, Jianhua Lu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Quality of experience (QoE) dynamic adaptive streaming over HTTP (DASH) enhanced deep-Q learning rate adaptation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1e2d1d8f516c4c01a67dafce896e8ed9
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id	oai:doaj.org-article:1e2d1d8f516c4c01a67dafce896e8ed9
record_format	dspace
spelling	oai:doaj.org-article:1e2d1d8f516c4c01a67dafce896e8ed92021-11-19T00:02:44ZQoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning2169-353610.1109/ACCESS.2018.2889999https://doaj.org/article/1e2d1d8f516c4c01a67dafce896e8ed92019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8594548/https://doaj.org/toc/2169-3536With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.Jie LiuXiaoming TaoJianhua LuIEEEarticleQuality of experience (QoE)dynamic adaptive streaming over HTTP (DASH)enhanced deep-Q learningrate adaptationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 8454-8469 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Quality of experience (QoE) dynamic adaptive streaming over HTTP (DASH) enhanced deep-Q learning rate adaptation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Quality of experience (QoE) dynamic adaptive streaming over HTTP (DASH) enhanced deep-Q learning rate adaptation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jie Liu Xiaoming Tao Jianhua Lu QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
description	With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.
format	article
author	Jie Liu Xiaoming Tao Jianhua Lu
author_facet	Jie Liu Xiaoming Tao Jianhua Lu
author_sort	Jie Liu
title	QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
title_short	QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
title_full	QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
title_fullStr	QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
title_full_unstemmed	QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
title_sort	qoe-oriented rate adaptation for dash with enhanced deep q-learning
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/1e2d1d8f516c4c01a67dafce896e8ed9
work_keys_str_mv	AT jieliu qoeorientedrateadaptationfordashwithenhanceddeepqlearning AT xiaomingtao qoeorientedrateadaptationfordashwithenhanceddeepqlearning AT jianhualu qoeorientedrateadaptationfordashwithenhanceddeepqlearning
_version_	1718420655110619136

QoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning

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