An innovative reinforcement learning-based framework for quality of service provisioning over multimedia-based SDN environments

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimediarich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks. Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches

Automatic fine-grained area detection for thin client systems

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LearnSDN: optimizing routing over multimedia-based 5G-SDN using machine learning

With the advent of 5G networks and beyond, there is an increasing demand to leverage Machine Learning (ML) capabilities and develop new and innovative solutions that could achieve efficient use of network resources and improve users' Quality of Experience (QoE). One of the key enabling technologies for 5G networks is Software Defined Networking (SDN) as it enables fine-grained monitoring and control of the network. Given the variety of dynamic networking conditions within 5G-SDN environments and the diversity of routing algorithms, an intelligent control of these strategies should exist to maximize the Quality of Service (QoS) provisioning of multimedia traffic with more stringent requirements without penalizing the performance of the background traffic. This paper proposes LearnSDN, an innovative ML-based solution that enables QoS provisioning over multimedia-based 5G-SDN environments. LearnSDN uses ML to learn the most convenient routing algorithm to be employed on the background traffic based on the dynamic network conditions in order to cater for the QoS requirements of the multimedia traffic. The performance of the proposed LearnSDN solution is evaluated under a realistic emulation-based SDN environment. The results indicate that LearnSDN outperforms other state-of-the-art solutions in terms of QoS provisioning, PSNR and MOS

Efficient Region-of-Interest Scalable Video Coding with Adaptive Bit-Rate Control

This work relates to the regions-of-interest (ROI) coding that is a desirable feature in future applications based on the scalable video coding, which is an extension of the H.264/MPEG-4 AVC standard. Due to the dramatic technological progress, there is a plurality of heterogeneous devices, which can be used for viewing a variety of video content. Devices such as smartphones and tablets are mostly resource-limited devices, which make it difficult to display high-quality content. Usually, the displayed video content contains one or more ROI(s), which should be adaptively selected from the preencoded scalable video bitstream. Thus, an efficient scalable ROI video coding scheme is proposed in this work, thereby enabling the extraction of the desired regions-of-interest and the adaptive setting of the desirable ROI location, size, and resolution. In addition, an adaptive bit-rate control is provided for the region-of-interest scalable video coding. The performance of the presented techniques is demonstrated and compared with the joint scalable video model reference software (JSVM 9.19), thereby showing significant bit-rate savings as a tradeoff for the relatively low PSNR degradation

Quality of service differentiation for multimedia delivery in wireless LANs

Delivering multimedia content to heterogeneous devices over a variable networking environment while maintaining high quality levels involves many technical challenges. The research reported in this thesis presents a solution for Quality of Service (QoS)-based service differentiation when delivering multimedia content over the wireless LANs. This thesis has three major contributions outlined below: 1. A Model-based Bandwidth Estimation algorithm (MBE), which estimates the available bandwidth based on novel TCP and UDP throughput models over IEEE 802.11 WLANs. MBE has been modelled, implemented, and tested through simulations and real life testing. In comparison with other bandwidth estimation techniques, MBE shows better performance in terms of error rate, overhead, and loss. 2. An intelligent Prioritized Adaptive Scheme (iPAS), which provides QoS service differentiation for multimedia delivery in wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. The total bandwidth to be allocated is estimated using MBE. The priority level of individual stream is variable and dependent on stream-related characteristics and delivery QoS parameters. iPAS can be deployed seamlessly over the original IEEE 802.11 protocols and can be included in the IEEE 802.21 framework in order to optimize the control signal communication. iPAS has been modelled, implemented, and evaluated via simulations. The results demonstrate that iPAS achieves better performance than the equal channel access mechanism over IEEE 802.11 DCF and a service differentiation scheme on top of IEEE 802.11e EDCA, in terms of fairness, throughput, delay, loss, and estimated PSNR. Additionally, both objective and subjective video quality assessment have been performed using a prototype system. 3. A QoS-based Downlink/Uplink Fairness Scheme, which uses the stereotypes-based structure to balance the QoS parameters (i.e. throughput, delay, and loss) between downlink and uplink VoIP traffic. The proposed scheme has been modelled and tested through simulations. The results show that, in comparison with other downlink/uplink fairness-oriented solutions, the proposed scheme performs better in terms of VoIP capacity and fairness level between downlink and uplink traffic

Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

Quality-of-Information Aware Sensing Node Characterisation for Optimised Energy Consumption in Visual Sensor Networks

Energy consumption is one of the primary concerns in a resource constrained visual sensor network (VSN) with wireless transceiving capability. The existing VSN design solutions under particular resource constrained scenarios are application-specific, whereas the degree of sensitivity of the resource constraints varies from one application to another. This limits the implementation of the existing energy efficient solutions within a VSN node, which may be considered to be a part of a heterogeneous network. This thesis aims to resolve the energy consumption issues faced within VSNs because of their resource constrained nature by proposing energy efficient solutions for sensing nodes characterisation. The heterogeneity of image capture and processing within a VSN can be adaptively reflected with a dynamic field-of-view (FoV) realisation. This is expected to allow the implementation of a generalised energy efficient solution that will adapt with the heterogeneity of the network. In this thesis, a FoV characterisation framework is proposed, which can assist design engineers during the pre-deployment phase in developing energy efficient VSNs. The proposed FoV characterisation framework provides efficient solutions for: 1) selecting suitable sensing range; 2) maximising spatial coverage; 3) minimising the number of required nodes; and 4) adaptive task classification. The task classification scheme proposed in this thesis exploits heterogeneity of the network and leads to an optimal distribution of tasks between visual sensing nodes. Soft decision criteria is exploited, and it is observed that for a given detection reliability, the proposed FoV characterisation framework provides energy efficient solutions which can be implemented within heterogeneous networks. In the post-deployment phase, the energy efficiency of a VSN for a given level of reliability can be enhanced by reconfiguring its nodes dynamically to achieve optimal configurations. Considering the dynamic realisation of quality-of-information (QoI), a strategy is devised for selecting suitable configurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacrificing the expected information retrieval reliability. By incorporating QoI awareness using peak signal-to-noise ratio-based representative metric, the distributed nature of the proposed self-reconfiguration scheme accelerates the decision making process. This thesis also proposes a unified framework for node classification and dynamic self-reconfiguration in VSNs. For a given application, the unified framework provides a feasible solution to classify and reconfigure visual sensing nodes based on their FoV by exploiting the heterogeneity of targeted QoI within the sensing region. From the results, it is observed that for the second degree of heterogeneity in targeted QoI, the unified framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability. Within the context of resource constrained VSNs, the substantial energy savings achieved by the proposed unified framework can lead to network lifetime enhancement. Moreover, the reliability analysis demonstrates suitability of the unified framework for applications that need a desired level of QoI