An Adaptive Neuro-Fuzzy Model for Quality Estimation in Wireless 2D/3D Video Streaming Systems
Ibrahim S. Alsukayti
Ibrahim S. Alsukayti*, Department of Computer Science, College of Computer, Qassim University, Buraidah, Saudi Arabia.
Manuscript received on November 19, 2019. | Revised Manuscript received on 28 November, 2019. | Manuscript published on December 10, 2019. | PP: 2291-2297 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6454129219/2019©BEIESP | DOI: 10.35940/ijitee.B6454.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Delivering high Quality of Experience (QoE) is essential to the success of today’s subscription for internet video streaming services. Quality of Service (QoS) metrics are considered by the research community as the most influential factor on video QoE. Therefore, establishing QoS-QoE correlation becomes critical for improving video QoE estimation. This paper presents experimental development of effective correlation between QoE and QoS for both 2D and 3D video streaming services. This is then used to build an objective QoE estimation model for real-time streaming of both 2D and 3D video contents over wireless networks. This model is based on using Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the perceived video QoE. The proposed QoE model was trained with a set of media and packet layers’ metrics, taking into account the effect of video content type, dimension, and different packet loss metrics. The performance of the proposed QoE estimation model shows a considerable estimation accuracy with a correlation coefficient of 92% and 0.167 RMSE.
Keywords: QoE, QoS, MOS, Quality Estimation, ANFIS, Video Streaming.
Scope of the Article: QOS And Resource Management