Georgios Exarchakos

Quality of Experience Models for Multimedia Streaming

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience QoE is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

When does lower bitrate give higher quality in modern video services

Due to the difficulties on approximating the human perception with algorithms, increasing the users Quality of Experience (QoE) in modern video services is a challenging task. But more than that, prior to estimating QoE, it is important to know how different types of network impairments actually affect the video quality. This paper takes a closer look at the relation between the network quality of service (QoS) and the video QoE degradation. Using a sophisticated network emulation environment, we benchmark a range of video types and video quality levels under controlled network conditions. Our analysis shows that, along with a number of expected situations come also some counterintuitive QoS-to-QoE conditions. We discuss ways in which a better understanding of the mutual influence between networks and video streams could lead to more efficient utilization of the Internet.

Instantaneous Video Quality Assessment for lightweight devices

Monitoring and controlling the users Quality of Experience (QoE) in modern video services is a challenging proposition, mainly due to the limitations of current video quality assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. On the other hand, the existing objective algorithms are either too complex or too inaccurate, particularly in the context of lightweight devices such as camera sensors or smart phones. This paper introduces a novel objective QoE algorithm, Instantaneous Video Quality Assessment (IVQA), that is comparably as accurate as the most heavyweight algorithm available in the literature but can also be run in real-time. This approach is tested against a selection of ten objective metrics and benchmarked with a subjective user dataset.

Machine Learning Approach for Quality of Experience Aware Networks

Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This process usually incorporates complex subjective studies that need to recreate the viewing conditions of the service in a controlled environment. In this paper we present Machine Learning techniques for modeling the dependencies of different network and application layer quality of service parameters to the QoE of network services using subjective quality feedback. These accurate QoE prediction models allow us to further develop a geometrical method for calculating the possible remedies per network stream for reaching the desired level of QoE. Finally we present a set of possible network techniques that can deliver the desired improvement to the multimedia streams.

Load-driven neighbourhood reconfiguration of Gnutella overlay

Unstructured P2P networks support distributed applications whose workload may vary significantly over time and between nodes. Self-optimizing systems try to keep the load in the network balanced despite the frequent load fluctuations. Several P2P systems exhibit a number of related features but fail to avoid centralisation under high-load situations. ERGO aims to balance the overloaded nodes by rewiring some of their incoming links to underloaded ones via a set of interconnected servers which index the underloaded nodes. In two simulated environments, ERGO load-balancing on Gnutella network increases the balanced nodes and network availability by preserving its efficiency and even reducing its messages.

Reduced reference image quality assessment via Boltzmann Machines

Monitoring and controlling the users perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzmann Machine Similarity Measure (RBMSim). RBMSim was evaluated on two benchmarked image databases with subjective studies, against objective IQA algorithms. The results show that its performance is comparable, or even better in some cases, with widely known FR IQA methods.

Estimations and Remedies for Quality of Experience in Multimedia Streaming

Managing multimedia network services in a User-centric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality of Experience (QoE) is such a metric, which captures many different aspects that compose the perception of quality. The drawback of using QoE is that due to its subjectiveness, accurate measurement necessitates execution of cumbersome subjective studies. In this work we propose a method that uses Machine Learning techniques to build QoE prediction models based on limited subjective data. Using those models we have developed an algorithm that generates the remedies for improving the QoE of observed multimedia stream. Selecting the optimal remedy is done by comparing the costs in resources associated to each of them. Coupling the QoE estimation and calculation of remedies produces a tool for effective implementation of a User-centric management loop for multimedia streaming services.

Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies, and Applications

The Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies, and Applications addresses the need for peer-to-peer computing and grid paradigms in delivering efficient service-oriented computing. This critical mass of the most sought after research serves as a collection of chapters facilitating a broad understanding of the subject matter useful to researchers and doctoral students working specifically in the deployment, implementation, and study of related topics including distributed computing, software engineering, Web services, and modeling of business processes.

Resource Sharing Architecture For Cooperative Heterogeneous P2P Overlays

Resource requirements and availability in heterogeneous networks may frequently vary over their lifetime; thus producing equally variant overloaded and under-loaded situations. Typical architectures cannot cope with the frequent availability fluctuation of reusable, non-replicable and highly dynamic resources (such as network capacity). This paper proposes an unstructured P2P overlay for sharing resources between underutilized and overloaded networks. Its aim is to satisfy the excessive resource demands of some networks by using free resources from others given the high failure rate and unstable availability of these resources in wide networks. We describe and analyze the proposed Capacity Sharing Overlay Architecture and show, with extensive simulations, its ability to provide remote underutilized capacity to underlying networks, even in the presence of high node failure rates, helping the networks to handle more user queries.

plexi: Adaptive re-scheduling web-service of time synchronized low-power wireless networks

Industrial IoT applications require highly dependable monitoring and actuation capabilities of remote interoperable low power devices. Time scheduling with channel hopping has been a well-attested mechanism to address these requirements in volatile environments. Yet, scheduling algorithms to date are not adaptive enough to changes in deployed applications and their environments. plexi is a restful web service API for monitoring and scheduling IEEE802.15.4e network resources hiding the complexity of schedule deployment and modification. On top, plexiflex allows any given scheduler to adapt to network performance changes by monitoring periodic data streams coming from the nodes. It triggers resource (de)allocation aiming at stable network performance. Both plexi API and plexiflex adaptive rescheduling algorithm allow for interoperability among devices, schedulers and applications. Experiments to real TSCH network deployments have shown significant gains of plexiflex compared to fixed offline scheduling. Via monitoring the interarrival time of those stream data packets, plexiflex can identify wiring new node joining and rewiring events and reschedule when and where is needed.