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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2016-01-01
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Series: | Mobile Information Systems |
Online Access: | http://dx.doi.org/10.1155/2016/4913216 |
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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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1721332928417366016 |