As Wireless Mesh Networks (WMNs) are being increasingly deployed, there is an increasing demand for new quality assessment mechanisms that allow service operators to evaluate and optimize the utilization of network resources, while ensuring a good quality level on multimedia applications as perceived by end-users. However, existing real-time assessment schemes for WMNs are not capable of capturing the actual quality of received multimedia content with regard to user perception. Therefore, it is not possible to assure the user experience of content services. To address this problem, this paper introduces the Hybrid Quality of Experience (HyQoE) Prediction, which is a quality estimator specially designed to assess realtime multimedia applications. HyQoE is designed based on the framework of the widely used Pseudo-Subjective Quality Assessment (PSQA) Tool which exploits Random Neural Network (RNN). Crucial extension work has been implemented to achieve our objectives. A performance evaluation verifies the effectiveness and advantages of HyQoE in predicting users’ perception of multimedia content in WMNs over existing subjective and hybrid methods.

As Wireless Mesh Networks (WMNs) have been increasingly deployed, where users can share, create and access videos with different characteristics, the need for new quality estimator mechanisms has become important because operators want to control the quality of video delivery and optimize their network resources, while increasing the user satisfaction. However, the development of in-service Quality of Experience (QoE) estimation schemes for Internet videos (e.g., real-time streaming and gaming) with different complexities, motions, Group of Picture (GoP) sizes and contents remains a significant challenge and is crucial for the success of wireless multimedia systems. To address this challenge, we propose a real-time quality estimator approach, HyQoE, for real-time multimedia applications. The performance evaluation in a WMN scenario demonstrates the high accuracy of HyQoE in estimating the Mean Opinion Score (MOS). Moreover, the results highlight the lack of performance of the well-known objective methods and the Pseudo-Subjective Quality Assessment (PSQA) approach.

The development of real-time quality estimator schemes for emerging Internet videos with different content types remains a significant challenge and is crucial for the success of wireless multimedia systems. However, currently in-service assessment schemes fail in capturing subjective aspects of multimedia content related to the user perception. Therefore, this paper proposes an on-the-fly parametric video quality estimator approach (called MultiQoE) for real-time video streaming applications. Experiments in a Wireless Mesh Network (WMN) scenario were carried out to show the accuracy, benefit, and impact of MultiQoE compared to widely used Quality of Experience (QoE) subjective, objective and parametric methods.