TY - JOUR
T1 - A Framework for QoE-Aware 3D Video Streaming Optimisation over Wireless Networks
AU - Politis, Ilias
AU - Lykourgiotis, Asimakis
AU - Dagiuklas, Tasos
N1 - Publisher Copyright:
© 2016 Ilias Politis et al.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84971377489&partnerID=8YFLogxK
U2 - 10.1155/2016/4913216
DO - 10.1155/2016/4913216
M3 - Article
AN - SCOPUS:84971377489
SN - 1574-017X
VL - 2016
JO - Mobile Information Systems
JF - Mobile Information Systems
M1 - 4913216
ER -