The rapid growth in mobile video consumption, driven by advancements in mobile devices and network infrastructure, has raised user expectations for seamless video Quality of Experience (QoE) despite improvements in video streaming, network impairments like packet loss, delay, jitter, and outages. For instance, outages can cause visual artifacts like freezing, jumping, and missing frames, which negatively affect user perception. Understanding the relationship between network performance and QoE is crucial for improving user satisfaction.

This thesis investigates the impact of network performance on mobile video QoE and proposes strategies to mitigate these effects. The objectives include: (1) understanding TCP/IP’s role in influencing QoE, (2) exploring the effects of Quality of Service (QoS) parameters such as delay, jitter, and packet loss on video quality, (3) analyzing the impact of network outages on QoE, and (4) developing a buffer-based solution to mitigate network disruptions.

The research employs theoretical modeling, controlled emulation experiments, and subjective assessments to evaluate QoE. The QoE Hourglass Model links network-layer parameters to user-perceived quality. Subjective tests, guided by ITU-T recommendations, use the Absolute Category Rating (ACR) method and Mean Opinion Scores (MOS) to assess video quality under various conditions. Additionally, the effectiveness of a sender buffer mechanism is tested through statistical analyses and user evaluations.

The findings reveal that network impairments, especially packet loss and delay variation, significantly degrade QoE. The QoE Hourglass Model provides a structured framework for understanding these effects. Experimental results show that higher frame rates and proactive buffering improve user perception. Perceptual Evaluation of Video Quality (PEVQ) and Temporal Quality Metric (TQM) measurements correlate with user ratings but are less accurate in predicting video freezes. The sender buffer mechanism effectively reduces freeze durations and enhances QoE during network outages.

This research emphasizes the impact of network impairments on video QoE and offers practical solutions, such as the sender buffer mechanism, to mitigate disruptions and enhance user satisfaction in video streaming.

With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of the video Quality of Experience (QoE). During live streaming, a network outage may result in video freezes and video jumps. To dampen the impact of a network outage on the video QoE, we propose the use of a well-sized sender buffer. We present the concept, derive key analytical relations, and perform a set of subjective tests. Based on those, we report a significant enhancement of user ratings due to the proposed sender buffer in the presence of network outages.

In Quality of Experience, the users' degrees of delight or annoyance are quantized by direct human interaction via subjective quality assessments. There are many factors that may influence the users' responses, and standards have been laid down to strictly control the monitoring conditions. In this paper, we analyze limitations of Mean Opinion Score (MOS) assessment to portray the influence of additional factors on user behavior while assessing multimedia content. We show that the frequency of watching online video content as well as the user delight with the content play a significant role in her final feedback. Our study emphasizes the need to use additional metrics along with MOS for content ratings to obtain a more accurate measure of the user experience. © 2019 The University of Lahore.

Background: Teaching students to critically appraise scientific literature is an important goal for a postgraduate research methods course. Objective: To investigate the application of checklists for assessing the scientific rigor of empirical studies support students in reviewing case study research and experiments. Methods:We employed an experimental design where 76 students (in pairs) used two checklists to evaluate two papers (reporting a case study and an experiment) each. We compared the students' assessments against ratings from more senior researchers. We also collected data on students' perception of using the checklists. Results: The consistency of students' ratings and the accuracy when compared to ratings from seniors varied. A factor seemed to be that the clearer the reporting, the easier it is for students to judge the quality of studies. Students perceived checklist items related to data analysis as difficult to assess. Conclusion: As expected, this study reinforces the needs for clear reporting, as it is important that authors write to enable synthesis and quality assessment. With clearer reporting, the novices performed well in assessing the quality of the empirical work, which supports its continued use in the course as means for introducing scientific reviews. © 2018 Association for Computing Machinery.

Quality of Experience (QoE) management is a prime topic of research community nowadays as video streaming, online gaming and security applications are completely reliant on the network service quality. Moreover, there are no standard models to map Quality of Service (QoS) into QoE. HTTP media streaming is primarily used for such applications due to its coherence with the Internet and simplified management. The most common video codecs used for video streaming are H.264/AVC and Google's VP8. In this paper, we have analyzed the performance of these two codecs from the perspective of QoE. The most common end-user medium for accessing video content is via home based wireless networks. We have emulated an error-prone wireless network with different scenarios involving packet loss, packet delay and delay variation. The focus of this paper is evaluating the end user satisfaction with the multimedia content by subjective assessment using Mean Opinion Score (MOS). We have observed the overall superiority of H.264/AVC but the VP8 codec strongly gains ground in highly error-prone networks in terms of user satisfaction. © 2017 IFIP.

In real-time video streaming, video quality can be degraded due to network performance issues. Among other artefacts, video freezing and video jumping are factors that influence user experience. Service providers, operators and manufacturers are interested in evaluating the quality of experience (QoE) objectively because subjective assessment of QoE is expensive and, in many user cases, subjective assessment is not possible to perform. Different algorithms have been proposed and implemented in this regard. Some of them are in the recommendation list of the ITU Telecommunication Standardization Sector (ITU-T). In this paper, we study the effect of the freezing artefact on user experience and compare the mean opinion score of these videos with the results of two algorithms, the perceptual evaluation of video quality (PEVQ) and temporal quality metric (TQM). Both metrics are part of the ITU-T Recommendation J.247 Annex B and C. PEVQ is a full-reference video quality metric, whereas TQM is a no-reference quality metric. Another contribution of this paper is the study of the impact of different resolutions and frame rates on user experience and how accurately PEVQ and TQM measure varying frame rates.

Multimedia applications have become the prime source of Internet traffic in recent years due to high bandwidth offered by almost all access mechanisms. This highlights the need of intense research efforts to improve the already demanding user perception levels for high-performance video delivery networks. Quality of Experience (QoE) based metrics are often used to quantify user satisfaction levels regarding an application or service. In this paper, we have analyzed the impact of variable frame sizes at link layer with different packet loss scenarios to evaluate the performance degradation of H.264 based live video streams from the end user perspective using subjective tests. We have focused on both subjective and objective quantitative measures to analyze the myth that smaller packets provide better quality in error-prone networks. We found that this assumption may not be true for some cases with a considerable packet loss ratio. Moreover, we observed that full-reference video assessment software like PEVQ predicts QoE to an acceptable extent, which allows cutting cost and effort coming with subjective evaluations. Keywords-Quality of Experience, Multimedia communication, Streaming media

Video streaming and multimedia applications are getting popular with the growth of networks. In real-time video streaming, video quality can be degraded due to network performance issues. Among other artifacts, freezing and frame dropping are factors that influence user experience. Service providers, operators, and researchers are interested to measure the Quality of Experience objectively. Different algorithms have been proposed and implemented in this regard. Some of them are in the recommendation list of the ITU Telecommunication Standardization Sector (ITU-T). In this paper, we study the effect of the freezing artifact on user experience and compare the mean opinion score (MOS) of these videos with the results of two algorithms, Perceptual Evaluation of Video Quality (PEVQ) and Temporal Quality Metric, both being part of ITU-T Recommendation J.247 Annex B and C, respectively. Another contribution of this paper is the investigation of the impact of different resolutions and frame rates on user experience.

Recently, user perception has been receiving increasingly much attention of service providers, operators, manufactures and researchers. They are interested to know how the users perceive the quality of multimedia streaming services. To avoid the fatigue of subjective testing, they are interested to model the Quality of Experience (QoE) based on Quality of Service (QoS) and other measurable parameters. However, these parameters to estimate the QoE may differ per service. In this paper, we propose a conceptual model of QoE in parallel with the classical internet architecture hourglass model. Moreover conditions are highlighted under which QoE can be evaluated objectively from QoS without entailing other factors. The main is goal to understand the factors that are playing a significant role for QoE. We also discuss and explain the role of the application on QoE.

The fluctuating performance of wireless and mobile networks has triggered the need for smart algorithms to assess the user perception, resulting from the quality of network services. While efforts have been done to model the user experience resulting from the network performance, there is still the need for practical methods to assess the user-perceived performance, in the real environment. In this work, we present a set of criteria to observe the user behavior on the Web, passively from the network-level. The criteria are based on the monitoring of TCP control flags and HTTP requests. Thus, information about user actions performed in the web browser can be inferred by monitoring the TCP termination flags and by keeping track of the HTTP requests. Along the way, we also present some anomalies observed in the TCP connection termination process, which may result in performance degradation of Web transfers.