Recently, video streaming services consumption has grown massively and is foreseen to increase even more in the future. The tremendous traffic usage has negatively impacted the network’s quality of service due to network congestion and end-to-end customers’ satisfaction represented by the quality of experience, especially during evening peak hours. This paper introduces an intelligent multimedia framework that aims to optimise the network’s quality of service and users’ quality of experience by taking into account the integration of Software-Defined Networking and Reinforcement Learning, which enables exploring, learning, and exploiting potential paths for video streaming flows. Moreover, an objective study was conducted to assess video streaming for various realistic network environments and under low and high traffic loads to obtain two quality of experience metrics; video multimethod assessment fusion and structural similarity index measure. The experimental results validate the effectiveness of the proposed solution strategy, which demonstrated better viewing quality by achieving better customers’ quality of experience, higher throughput and lower data loss compared with the currently existing solutions.

Software Defined Network (SDN) provides a new fine-grained interface enables the routing algorithm to have an a global view of the network throughputs, connectivity and flows at the data-path. This paper aims to provide a novel approach for dynamic routing algorithm for Software Defined Network in Wide Area Network (SDN-WAN); based on using a modified shortest-widest path algorithm with a fine-grained statistical method from the OpenFlow interface, called Shortest-Feasible OpenFlow Path (SFOP). This algorithm is designed to identify the optimal route from source to destination, providing efficient utilization of the SDN-WAN resources. It achieves this aim by considering both the flow requirements and the current state of the network. SFOP computes the optimal path which provides the feasible bandwidth with the lowest hop count (delay). That will present better stability in SDN communication, QoS, and usage of available resources. Moreover, this algorithm will be the base for an SDN controller because it extracts the widest available bandwidth from source to destination for a single path. It enables the controller to decide whether it is enough to use this simple algorithm only, or if a more complicated algorithm that provides larger bandwidth such as multiple-path algorithms is needed. Finally, a testbed has been implemented using MATLAB Simulator, Pox controller, and Mininet emulator will be discussed. The latency comparison of SFOP algorithm with three other algorithm's latencies shows that this algorithm finds better latency for an optimal path. Evidence will be shown that demonstrates that SFOP has good stability in dynamic changes of SDN-WAN.

This paper was inspired by looking at the central role of emotion in the learning process, its impact on students’ performance; as well as the lack of affective computing models to detect and infer affective-cognitive states in real time for students with and without Asperger Syndrome (AS). This model overcomes gaps in other models that were designed for people with autism, which needed the use of sensors or physiological instrumentations to collect data. The model uses a webcam to capture students’ affective-cognitive states of confidence, uncertainty, engagement, anxiety, and boredom. These states have a dominant effect on the learning process. The model was trained and tested on a natural-spontaneous affective dataset for students with and without AS, which was collected for this purpose. The dataset was collected in an uncontrolled environment and included variations in culture, ethnicity, gender, facial and hairstyle, head movement, talking, glasses, illumination changes, and background variation. The model structure used deep learning (DL) techniques like convolutional neural network and long short-term memory. The DL is the-state-of-art tool that used to reduce data dimensionality and capturing non-linear complex features from simpler representations. The affective model provides reliable results with accuracy 90.06%. This model is the first model to detected affective states for adult students with AS without physiological or wearable instruments. For the first time, the occlusions in this model, like hand over face or head were considered an important indicator for affective states like boredom, anxiety, and uncertainty. These occlusions have been ignored in most other affective models. The essential information channels in this model are facial expressions, head movement, and eye gaze. The model can serve as an aided-technology for tutors to monitor and detect the behaviors of all students at the same time and help in predicting negative affective states during learning process.

This paper describes the Web Real-Time Communication (WebRTC) technology and the implementation of its clients and server. The main aim is to design and implement WebRTC video conferencing between browsers in real implementation using Chrome and (Wired & WiFi) of LAN & WAN networks. Also, an evaluation of CPU performance, bandwidth consumption and Quality of Experience (QoE) was achieved. Moreover, a signalling channel between browsers using the WebSocket protocol via Node.js platform has been created and executed. This paper will give web developer an opportunity to comprehend the WebRTC technology, as well as to understand how to design WebRTC video conferencing.

The energy inefficiency of electronic devices is the major limiting factor for the adoption of these devices in Machine-to-Machine Communications (M2M)/Internet of Things (IoT). In order to address this, different emerging M2M communications standardisation organisations have proposed perceived energy efficient protocols. The essence of this paper is to discuss the energy implications of different M2M communication standards on the lifespan of the electronic devices that are to be used for the M2M communications.