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Nowadays, video streaming has become one of the most dominant services due to increasing interest in watching online television programs and video-on-demand activities. Providing this service requires high quality, high speed and high-capacity network infrastructures. In this work, we propose a data-drivenbased fuzzy logic inference algorithm for quality of experience (QoE) modeling of video streaming services in Addis Ababa City LTE network. The proposed fuzzy logic inference system model is used to measure the user perception from the key quality of indicator (KQI) parameters. The model is essential to replace conventional subjective measurement techniques that are costly and inefficient. In addition, the proposed fuzzy logic inference system model is helpful for business decision making, network planning and optimization activities. To analyze the performance of the proposed model, we consider main KQI parameters such as video streaming start success rate, video streaming start delay, video streaming play disconnection rate, video streaming stall frequency and video streaming stalled time rate. We perform numerical simulation to analyze the proposed model and to validate the impacts of main KQI parameters on the quality of experience in LTE video streaming services. The simulation results show that video streaming stall frequency and video streaming start delay rate play a major impact on user perception by 33% and 25%, respectively. Besides, validation of the results shows that the proposed fuzzy logic inference system model is accurate, consistent and linear compared to currently existing models. [ABSTRACT FROM AUTHOR]

With the explosive growth of mobile video applications, analysis of video quality becomes increasingly important because it is an important Key Performance Indicator (KPI) for Quality of Experience (QoE). In this paper, a framework for non-reference video quality analysis is proposed and applied to Video Telephony (VT) in LTE networks. Three metrics, blockiness, blur and freezing, are used to estimate the MOS. Blockiness is detected by taking the H.264 codec features into account, blur is estimated by utilizing the percentage of noticeable blurred edges in each frame, and freezing is evaluated by using a sigmoid function to mimic the effect of different freezing duration on the Human Visual System (HVS). Furthermore, the three metrics are combined into one objective MOS by considering different weighting factors and using the linear curve fitting. Above 90% correlation is achieved between the objective MOS score and subjective MOS score. [ABSTRACT FROM AUTHOR]