With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.INDEX TERMS HTTP adaptive streaming, QoE modeling, TCP, video quality assessment.

The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

Abstract-We present a survey of psychophysiology-based assessment for Quality of Experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarise multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardised experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research.

No abstract

No abstract

Abstract-We present a framework for the analysis of frame synchronization based on synchronization words (SWs), where the detection is based on the following sequential algorithm. The received samples are observed over a window of length equal to the SW; over this window, a metric (e.g., correlation) is computed; an SW is declared if the computed metric is greater than a proper threshold, otherwise the observation window is time-shifted one sample. We assume a Gaussian channel, antipodal signaling, equally distributed data symbols, and coherent detection, where soft values are provided to the frame synchronizer. We state the problem starting from the hypothesis testing theory, deriving the optimum metric [optimum likelihood ratio test (LRT)] according to the Neyman-Pearson lemma. When the data distribution is unknown, we design a simple and effective test based on the generalized LRT (GLRT). We also analyze the performance of the commonly used correlation metric, both with "hard" and "soft" values at the synchronizer input. We show that synchronization can be greatly improved by using the LRT and GLRT metrics instead of correlation and that, among correlation-based tests, sometimes hard correlation is better than soft correlation. The obtained closed-form expressions allow the derivation of the receiver operating characteristic (ROC) curves for the LRT and GLRT synchronizers, showing a remarkable gain with respect to synchronization based on correlation metric.

The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs Appl (2017) 76:22243-22266 focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers.

Abstract-The Quality of Experience (QoE) and Quality of Service (QoS) provided in the healthcare sector are critical in evaluating the reliable delivery of the healthcare services provided. Medical images and videos play a major role in modern e-health services and have become an integral part of medical data communication systems. The quality evaluation of medical images and videos is an essential process, and one of the ways of addressing it is via the use of quality metrics. In this paper, we evaluate the performance of seven state of the art video quality metrics with respect to compressed medical ultrasound video sequences. We study the performance of each video quality metric in representing the diagnostic quality of the video, by evaluating the correlation of each metric with the subjective opinions of medical experts. The results indicate that the Visual Information Fidelity (VIF), Structural Similarity Index Metric (SSIM), and Universal Quality Index (UQI) metrics show good correlation with the subjective scores provided by medical experts. The tests also investigate the performance of the emerging video compression standard, High Efficiency Video Coding (HEVC), for medical ultrasound video compression. The results show that, using HEVC, a diagnostically reliable compressed ultrasound video can be obtained for compression with values of the quantization parameter, QP, upto 35.Index Terms-Medical video quality evaluation, HEVC, service science, objective & subjective video quality assessment, medical ultrasound videos, video compression.