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.

Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples' lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3% when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach's effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy.