QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

Abstract

Cellular networks are constantly evolving to support the new context and demands of users with increasing internet traffic. The key to a service provider's success is a direct response to endpoints to retain existing users and attract new users. For this reason, Mobile Network Operators (MNOs) must reach user satisfaction under a user-centered perspective with Quality of Experience (QoE) to achieve a Return on Investment (RoI). In the latest QoE modelling, our previous method performed QoE estimation and synthesis using an Artificial Neural Network (ANN). Nevertheless, such work did not prove the characteristics of the QoE model built on different working situations. The essential features of the area and user diversities need to be considered in detail to summarize the proper model. In this paper, the proposed framework focuses on analyzing QoE model characteristics under different operating situations for comprehensive implementation. The experimental results reveal that the created QoE models from areas of different densities affect the different predictive variables. Inter-Rater Reliability (IRR) analysis exposes the reliability of datasets gathered from users of different groups to distinguish the effects on the response of significant parameters. Additionally, the summary specifies the characteristics of the appropriate model by area and user group as a guideline for improving the multimedia services.

An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience

“…The result shows that the poor, fair, good and excellent experience of objective calculation, which are 0.75%, 1.27%, 11.84%, 86.01%, respectively, are similar to the subjective evaluation, which are 1.10%, 1.88%, 13.32%, 83.70%, respectively, so that the results of the proposed method are reliable. In addition, Figure 10 shows the whole QoE results distribution comparison between questionnaires and other QoE model from reference [11]. The result shows that the poor, fair, good and excellent experience of objective calculation are far from the calculation results of other QOE models, which are 5.90%, 6.50%, 15.20%, 72.40%, respectively, so that the results of the proposed method are reliable.…”

“…Besides, Anwar et al, compared K-Nearest Neighbors, Support Vector Machines, Decision Trees and Logistic Regression in terms of perceptual quality to consider the QoE estimation model for Virtual Reality (VR) services [10]. Anchuen et al, summarizes the QoE model characteristics for in four popular multimedia services, YouTube, Facebook, Line, and Web browser, based on area diversity and user diversity and creates QoE model with the ANN Method [11]. Boz, Eren, et al, propose a mobile QoE prediction in the field, which can report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements [12].…”

Performance analysis for a suitable propagation model in outdoor with 2.5 GHz band

“…It is utilized to determine the signal's pathloss in the free area without obstacles when there is no reflection, diffraction, and attenuation. It is a perfect model for the enormous distance between the transmitter and receiver [19], [20]. Because radio waves expand out according to the inverse square law, FSPL increases with the square of the separation between the sender and receiver.…”

“…16 up to a 3,500 MHz band. As a result, the SUI model was declared for the" multipoint microwave distribution system (MMDS)" in 2,500 MHz to 2,700 MHz frequency bands in the United States [20], [21].…”

“…It came from massive data measured from Japan, and all recent models are established from the Okumura model. The frequency limit included by this model is 200-1,925 MHz, with a base station height of 30-100 m and a receiver antenna height of 1-3 m. Moreover, the sender and receiver can be separated by up to 100 km [20], [22]. For this model is presented in (8):…”