One of the main problems faced by ad hoc networks is providing specific quality of service guarantees for multimedia applications, mainly due to factors such as radio signal fading and node mobility. Since mesh networks are a special type of ad hoc network, they inherit these networks' problems. This paper's main goal is to present OLSR-MD, an extension to OLSR (Optimized Link State Routing), to provide quality of service based on link delay measurements. An evaluation of OLSR-MD in a mesh network to be deployed at the Federal University of Pará, by means of ns2 (version 2.30) simulations, showed that this protocol performed better than other OLSR based alternatives studied in the simulations.
Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user's satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a nonintrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks' impairments, videos' characteristics, and users' perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user's perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.
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.Index Terms-Multimedia, wireless networks, video streaming, quality of experience, video quality prediction, random neural networks, wireless mesh networks.
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.
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