Optimising QoE for Scalable Video multicast over WLAN

International audienceQuality of Experience (QoE) is the key to success for multimedia applications and perceptual video quality is one of the important component of QoE. A recent video encoding scheme called Scalable Video Coding (SVC) provides the flexibility and the capability to adapt the video quality to varying network conditions and heterogeneous users. In this paper, we focus on SVC multicast over IEEE 802.11 networks. Traditionally, multicast uses the lowest modulation resulting in a video with only base quality even for users with good channel conditions. To optimize QoE, we propose to use multiple multicast sessions with different transmission rates for different SVC layers. The goal is to provide at least the multicast session with acceptable quality to users with bad channel conditions and to provide additional multicast sessions having SVC enhancement layers to users with better channel conditions. The selection of modulation rate for each SVC layer and for each multicast session is achieved with binary integer linear programming depending on network conditions with a goal to maximize global QoE. Results show that our algorithm maximizes global QoE by providing highest quality videos to users with good channel conditions and by guaranteeing at least acceptable QoE for all users

Mobile Content Delivery Network Design and Implementation

In this thesis, a novel concept of Mobile Content Delivery Network is designed and implemented in a real testbed with the target of flexibly adapting the video caching in the cellular network to the users dynamics. New challenges are discussed and practical considerations for wide-scale deployment in next generation cellular networks are drawn

Final Specification of Cooperative Functionalities

This deliverable presents the specification of the final version of the Cooperative AP Functionalities that have been designed in the context of Work Package (WP) 4 of the Wi-5 project. In detail, we present a general cooperative framework that includes functionalities for a Radio Resource Management (RRM) algorithm, which provides channel assignment and transmit power adjustment strategies, an AP selection policy, which also provides horizontal handover, and a Radio Access Technology (RAT) selection solution for vertical handover. The RRM algorithm achieves an important improvement for network performance in terms of several parameters through the channel assignment approach and the transmit power adjustment. The AP selection solution extends the approach presented in deliverables D4.1 and D4.2 and is based on a centralised potential game, which optimises the distribution of the so-called Fittingness Factor (FF) parameter among the Wi-Fi users. Such a parameter efficiently matches the suitability of the available spectrum resource to the users’ application requirements. Moreover, the RAT selection solution extends the AP selection algorithm towards vertical handover functionality including 3G/4G networks. The assessment of the newest algorithms developed in the context of WP4 is illustrated in this deliverable through the analysis of several performance results in a simulated environment against other strategies found in the literature. Finally, the set of smart AP functionalities developed in the context of WP3, implemented on the Wi5 APs and on the Wi-5 controller, and their use in the proposed algorithms are illustrated. Specifically, this deliverable describes how these functionalities can enable the correct deployment of the proposed cooperative AP solutions in realistic scenarios. Therefore, the main novel contributions of this deliverable are i) the strengthening of the AP selection algorithm, ii) the design and assessment of a new algorithm for vertical handover and iii) the presentation of the finalised integration of the cooperative AP functionalities of the Wi-5 system

Cross-layer optimisation of quality of experience for video traffic

Realtime video traffic is currently the dominant network traffic and is set to increase in volume for the foreseeable future. As this traffic is bursty, providing perceptually good video quality is a challenging task. Bursty traffic refers to inconsistency of the video traffic level. It is at high level sometimes while is at low level at some other times. Many video traffic measurement algorithms have been proposed for measurement-based admission control. Despite all of this effort, there is no entirely satisfactory admission algorithm for variable rate flows. Furthermore, video frames are subjected to loss and delay which cause quality degradation when sent without reacting to network congestion. The perceived Quality of Experience (QoE)-number of sessions trade-off can be optimised by exploiting the bursty nature of video traffic. This study introduces a cross-layer QoE-aware optimisation architecture for video traffic. QoE is a measure of the user's perception of the quality of a network service. The architecture addresses the problem of QoE degradation in a bottleneck network. It proposes that video sources at the application layer adapt their rate to the network environment by dynamically controlling their transmitted bit rate. Whereas the edge of the network protects the quality of active video sessions by controlling the acceptance of new sessions through a QoE-aware admission control. In particular, it seeks the most efficient way of accepting new video sessions and adapts sending rates to free up resources for more sessions whilst maintaining the QoE of the current sessions. As a pathway to the objective, the performance of the video flows that react to the network load by adapting the sending rate was investigated. Although dynamic rate adaptation enhances the video quality, accepting more sessions than a link can accommodate will degrade the QoE. The video's instantaneous aggregate rate was compared to the average aggregate rate which is a calculated rate over a measurement time window. It was found that there is no substantial difference between the two rates except for a small number of video flows, long measurement window, or fast moving contents (such as sport), in which the average is smaller than the instantaneous rate. These scenarios do not always represent the reality. The finding discussed above was the main motivation for proposing a novel video traffic measurement algorithm that is QoE-aware. The algorithm finds the upper limit of the video total rate that can exceed a specific link capacity without the QoE degradation of ongoing video sessions. When implemented in a QoE-aware admission control, the algorithm managed to maintain the QoE for a higher number of video session compared to the calculated rate-based admission controls such as the Internet Engineering Task Force (IETF) standard Pre-Congestion Notification (PCN)-based admission control. Subjective tests were conducted to involve human subjects in rating of the quality of videos delivered with the proposed measurement algorithm. Mechanisms proposed for optimising the QoE of video traffic were surveyed in detail in this dissertation and the challenges of achieving this objective were discussed. Finally, the current rate adaptation capability of video applications was combined with the proposed QoE-aware admission control in a QoE-aware cross-layer architecture. The performance of the proposed architecture was evaluated against the architecture in which video applications perform rate adaptation without being managed by the admission control component. The results showed that our architecture optimises the mean Mean Opinion Score (MOS) and number of successful decoded video sessions without compromising the delay. The algorithms proposed in this study were implemented and evaluated using Network Simulator-version 2 (NS-2), MATLAB, Evalvid and Evalvid-RA. These software tools were selected based on their use in similar studies and availability at the university. Data obtained from the simulations was analysed with analysis of variance (ANOVA) and the Cumulative Distribution Functions (CDF) for the performance metrics were calculated. The proposed architecture will contribute to the preparation for the massive growth of video traffic. The mathematical models of the proposed algorithms contribute to the research community

Middleware de comunicações para a internet móvel futura

Doutoramento em Informática (MAP-I)A evolução constante em novas tecnologias que providenciam suporte à forma como os nossos dispositivos se ligam, bem como a forma como utilizamos diferentes capacidades e serviços on-line, criou um conjunto sem precedentes de novos desafios que motivam o desenvolvimento de uma recente área de investigação, denominada de Internet Futura. Nesta nova área de investigação, novos aspectos arquiteturais estão ser desenvolvidos, os quais, através da re-estruturação de componentes nucleares subjacentesa que compõem a Internet, progride-a de uma forma capaz de não são fazer face a estes novos desafios, mas também de a preparar para os desafios de amanhã. Aspectos chave pertencendo a este conjunto de desafios são os ambientes de rede heterogéneos compostos por diferentes tipos de redes de acesso, a cada vez maior mudança do tráfego peer-to-peer (P2P) como o tipo de tráfego mais utilizado na Internet, a orquestração de cenários da Internet das Coisas (IoT) que exploram mecanismos de interação Maquinaa-Maquina (M2M), e a utilização de mechanismos centrados na informação (ICN). Esta tese apresenta uma nova arquitetura capaz de simultaneamente fazer face a estes desafios, evoluindo os procedimentos de conectividade e entidades envolvidas, através da adição de uma camada de middleware, que age como um mecanismo de gestão de controlo avançado. Este mecanismo de gestão de controlo aproxima as entidades de alto nível (tais como serviços, aplicações, entidades de gestão de mobilidade, operações de encaminhamento, etc.) com as componentes das camadas de baixo nível (por exemplo, camadas de ligação, sensores e atuadores), permitindo uma otimização conjunta dos procedimentos de ligação subjacentes. Os resultados obtidos não só sublinham a flexibilidade dos mecanismos que compoem a arquitetura, mas também a sua capacidade de providenciar aumentos de performance quando comparados com outras soluÇÕes de funcionamento especÍfico, enquanto permite um maior leque de cenáios e aplicações.The constant evolution in new technologies that support the way our devices are able to connect, as well the way we use available on-line services and capabilities, has created a set of unprecedented new challenges that motivated the development of a recent research trend known as the Future Internet. In this research trend, new architectural aspects are being developed which, through the restructure of underlying core aspects composing the Internet, reshapes it in a way capable of not only facing these new challenges, but also preparing it to tackle tomorrow’s new set of complex issues. Key aspects belonging to this set of challenges are heterogeneous networking environments composed by di↵erent kinds of wireless access networks, the evergrowing change from peer-to-peer (P2P) to video as the most used kind of traffic in the Internet, the orchestration of Internet of Things (IoT) scenarios exploiting Machine-to-Machine (M2M) interactions, and the usage of Information-Centric Networking (ICN). This thesis presents a novel framework able to simultaneous tackle these challenges, empowering connectivity procedures and entities with a middleware acting as an advanced control management mechanism. This control management mechanism brings together both high-level entities (such as application services, mobility management entities, routing operations, etc.) with the lower layer components (e.g., link layers, sensor devices, actuators), allowing for a joint optimization of the underlying connectivity and operational procedures. Results highlight not only the flexibility of the mechanisms composing the framework, but also their ability in providing performance increases when compared with other specific purpose solutions, while allowing a wider range of scenarios and deployment possibilities

Mobile Edge Cloud: Intelligent deployment and services for 5G Indoor Network

This thesis was submitted for the award of doctor of Philosophy and was awarded by Brunel University LondonFifth-Generation (5G) mobile networks are expected to perform according to the stringent performance targets assigned by standardization committees. Therefore, significant changes are proposed to the network infrastructure to achieve the expected performance levels. Network Function Virtualization, cloud computing and Software Defined Networks are some of the main technologies being utilised to ensure flexible network design, with optimum performance and efficient resource utilization. The aforementioned technologies are shifting the network architecture into service-based rather device-based architecture. In this regard, this thesis provides experimental investigation, design, implementation and evaluation of various multimedia services along with integration design and caching solution for 5G indoor network. The multimedia services are targeting the enhancement of UEs’ Quality of Experience, by exploiting the intelligence offered by the synergy between SDN and NFV technologies, to design and develop new multimedia solutions with improved QoE. The caching solution is designed to achieve a good trade-off between latency reduction and resource utilization that satisfies efficient network performance and resource utilization. The proposed network integration design targets deploying IoRL gNB with its innovative intelligent services. It have successfully achieved lower overhead signalling compared to the traditional network architectures. Whilst all of the proposed solutions have proven to provide enhancement to the system performance, the testing results for the multimedia services showed high QoS performance parameters in the form of zero packet loss due to route switching, very high throughput and 0.03 ms jitter. The caching solution test results provided up to 300% server utilization improvement (based on the deployed scenario) with negligible extra delay cost (0.5ms). As for the proposed integration design, the quantification of the performance enhancement is represented by the amount of the reduced overhead signalling. In the case of Intra-secondary gNB handover within the same Main eNB, the back-haul signalling for the AMF was reduced 100% while the overall overhead signalling is reduced by 50% compared to traditional deployment architecture.European Union’s Horizon 2020 research progra

Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks

Given the emergence of mobile cloud computing (MCC), its associated energy implications are witnessed at larger scale. With offloading computationally intensive tasks to the cloud datacentres being the basic concept behind MCC, most of the mobile terminal resources participating in the MCC collaborative execution are wasted as they remain idle until the mobile terminals receive responses from the datacentres. This is an additional wastage of resources alongside the cloud resources are already being addressed as massive energy consumers. Though the energy consumed of the idle mobile resources is insignificant in comparison with the cloud counterpart, such consumptions have drastic impacts on the mobile devices causing unnecessary battery drains. To this end, this paper proposes Mobilouds which encompass a multi-tier processing architecture with various levels of process cluster capacities and a software application to manage energy efficient utilization of such process clusters. Our proposed Mobilouds framework encourages the mobile device participation in the MCC collaborative execution, thereby reduces the presence of idle mobile resources and utilizes such idle resources in the actual task execution. Our performance evaluation results demonstrate that the Mobilouds framework offers the most energy-time balancing process clusters for task execution by effectively utilizing the available resources, in comparison with an entire cloud offloading strategy using 5G/4G networks

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication