A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks

The delivery of three-dimensional immersive media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics, and user terminal requirements, as well as user’s context. This paper proposes a framework for quality of experience-a...

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Main Authors: Ilias Politis, Asimakis Lykourgiotis, Tasos Dagiuklas
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Mobile Information Systems
Online Access:http://dx.doi.org/10.1155/2016/4913216
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spelling doaj-f26485fcb00e418e8e804e2247d189f32021-07-02T09:45:43ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2016-01-01201610.1155/2016/49132164913216A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless NetworksIlias Politis0Asimakis Lykourgiotis1Tasos Dagiuklas2CONES Research Group, Hellenic Open University, 26335 Patras, GreeceCONES Research Group, Hellenic Open University, 26335 Patras, GreeceCONES Research Group, Hellenic Open University, 26335 Patras, GreeceThe delivery of three-dimensional immersive media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics, and user terminal requirements, as well as user’s context. This paper proposes a framework for quality of experience-aware delivering of three-dimensional video across heterogeneous wireless networks. The proposed architecture combines a Media-Aware Proxy (application layer filter), an enhanced version of IEEE 802.21 protocol for monitoring key performance parameters from different entities and multiple layers, and a QoE controller with a machine learning-based decision engine, capable of modelling the perceived video quality. The proposed architecture is fully integrated with the Long Term Evolution Enhanced Packet Core networks. The paper investigates machine learning-based techniques for producing an objective QoE model based on parameters from the physical, the data link, and the network layers. Extensive test-bed experiments and statistical analysis indicate that the proposed framework is capable of modelling accurately the impact of network impairments to the perceptual quality of three-dimensional video user.http://dx.doi.org/10.1155/2016/4913216
collection DOAJ
language English
format Article
sources DOAJ
author Ilias Politis
Asimakis Lykourgiotis
Tasos Dagiuklas
spellingShingle Ilias Politis
Asimakis Lykourgiotis
Tasos Dagiuklas
A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
Mobile Information Systems
author_facet Ilias Politis
Asimakis Lykourgiotis
Tasos Dagiuklas
author_sort Ilias Politis
title A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
title_short A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
title_full A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
title_fullStr A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
title_full_unstemmed A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
title_sort framework for qoe-aware 3d video streaming optimisation over wireless networks
publisher Hindawi Limited
series Mobile Information Systems
issn 1574-017X
1875-905X
publishDate 2016-01-01
description The delivery of three-dimensional immersive media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics, and user terminal requirements, as well as user’s context. This paper proposes a framework for quality of experience-aware delivering of three-dimensional video across heterogeneous wireless networks. The proposed architecture combines a Media-Aware Proxy (application layer filter), an enhanced version of IEEE 802.21 protocol for monitoring key performance parameters from different entities and multiple layers, and a QoE controller with a machine learning-based decision engine, capable of modelling the perceived video quality. The proposed architecture is fully integrated with the Long Term Evolution Enhanced Packet Core networks. The paper investigates machine learning-based techniques for producing an objective QoE model based on parameters from the physical, the data link, and the network layers. Extensive test-bed experiments and statistical analysis indicate that the proposed framework is capable of modelling accurately the impact of network impairments to the perceptual quality of three-dimensional video user.
url http://dx.doi.org/10.1155/2016/4913216
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