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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.

The Novel Enablers for Cloud Slicing (NECOS) project addresses the limitations of current cloud computing infrastructures to respond to the demand for new services, as presented in two use-cases, that will drive the whole execution of the project. The first use-case is focused on Telco service provider and is oriented towards the adoption of cloud computing in their large networks. The second use-case is targeting the use of edge clouds to support devices with low computation and storage capacity. The envisaged solution is based on a new concept, the Lightweight Slice Defined Cloud (LSDC), as an approach that extends the virtualization to all the resources in the involved networks and data centers and provides uniform management with a high-level of orchestration. In this position paper, we discuss the motivation, objectives, architecture, research challenges (and how to overcome them) and initial efforts for the NECOS project.

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.

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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.

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