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.

With the growth of the mobile internet, the popularity of multimedia services and applications have increased rapidly. As a result, end-users become quality-conscious. To fulfill the users' expectations, the study of quality of experience (QoE) is becoming very important for both researchers and service providers. This paper analyses the impact on perceived quality of received videos encoded with the H.264 baseline profile, which is suitable for mobile video, and streamed through an emulated network with packet loss and packet delay variation. To evaluate the video QoE, tests are conducted on a mobile device and on a laptop. The users' responses show that the baseline profile of H.264 is very sensitive to packet loss and packet delay variation. Moreover, there is no considerable impact on users' perception either test is conducted on the mobile device or on the laptop by playing the same resolution video.

Pappret beskriver sammanhang mellan förluster / fördröjningsvariation och s k "Quality of Experience", och visar dessutom att terminalen (bärbar dator eller smartphone) inte spelar någon särskild roll i kvalitetsutvärderingen genom slutanvändare.

Quality of experience is getting the attention of the research community as well as the industry. In case of real-time streaming, packet loss, delay and jitter degrades the video quality. The player buffer can be emptied due to long delay, which freezes video at playout, while resuming the streaming video content causes jumps to current location. There is the question, how user is going to react to such kind of artifacts with respect to location where they arise. We collected user ratings for videos showing the artifacts due to delay variation as well as freezes and jumps at different locations. We also verified these results with Perceptual Evaluation of Video Quality (PEVQ) application. For delay and delay variation case, the PEVQ results are aligning to the human rating, but both differ in freeze-and-jump case. The users' responses in case of freeze-and-jump shows interesting results with respect to location.

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.

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.