Autors: Kougioumtzidis, G. V., Vlahov, A. G., Poulkov, V. K., Lazaridis, P.I., Zaharis, Z.D.
Title: Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks
Keywords: Deep learning; deep neural network (DNN)

Abstract: 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.

References

    Issue

    IEEE Access, vol. 11, pp. 143514-143529, 2023, United States, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/ACCESS.2023.3343846

    Copyright IEEE

    Цитирания (Citation/s):
    1. Hoßfeld T.; Pérez P., "A Theoretical Framework for Provider's QoE Assessment using Individual and Objective QoE Monitoring", 2024 16th International Conference on Quality of Multimedia Experience, QoMEX 2024, pp. 235-241, 2024, DOI: 10.1109/QoMEX61742.2024.10598265. - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science