TY - JOUR
T1 - Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks
AU - Kougioumtzidis, Georgios
AU - Vlahov, Atanas
AU - Poulkov, Vladimir K.
AU - Lazaridis, Pavlos I.
AU - Zaharis, Zaharias D.
N1 - Funding Information:
This research was supported by the European Union, through the Horizon 2020 Marie Skłodowska-Curie Innovative Training Networks Programme ''Mobility and Training for beyond 5G Ecosystems (MOTOR5G)'' under grant agreement no. 861219, and the ''Intelligent Communication Infrastructure Laboratory'' at Sofia Tech Park, Sofia, Bulgaria.
Publisher Copyright:
© 2013 IEEE.
PY - 2023/12/22
Y1 - 2023/12/22
N2 - Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications.
AB - Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications.
KW - Deep learning
KW - deep neural network (DNN)
KW - long short-term memory (LSTM)
KW - open radio access network (Open RAN)
KW - QoE prediction
KW - quality of experience (QoE)
KW - recurrent neural network (RNN)
KW - virtual reality (VR)
UR - http://www.scopus.com/inward/record.url?scp=85181569710&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3343846
DO - 10.1109/ACCESS.2023.3343846
M3 - Article
AN - SCOPUS:85181569710
VL - 11
SP - 143514
EP - 143529
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 10363177
ER -
