One of the paramount research questions in the scientific community today is how to remotely assess user quality of experience (QoE) for a specific service. To this end, various user QoE assessment models have been developed; however, they are mostly based on the data gathered from controlled environment experimentation. The aim of this research was to model user QoE for the User Datagram Protocol-based video streaming service from the results of uncontrolled subjective tests. Specifically, using fuzzy logic, we have correlated the values of three objective network parameters (the packet loss rate and the number and duration of packet loss occurrences in one streaming session) with test subjects’ subjective perception about perceived quality distortions. The dependencies between different values of the parameters and the subjects’ perception of video quality were used to develop a no reference objective video quality assessment model for assessing user QoE. The key distinguishing feature of the developed model lays in the process of subjective evaluation, which was conducted with a panel of 602 test subjects who evaluated the quality of 1-h video in home environments. For this purpose, 72 different test sequences were prepared for rating. We showed that a strong positive linear relationship exists between the assessed QoE of the model and the Mean Opinion Scores of the subjects (a Pearson correlation coefficient equal to 0.8841).

Multimedia content delivery has become one of the pillar services of modern day mobile and fixed networks. The variety of devices, platforms, and content providers together with increasing network capacity has impacted the popularity of this type of service. Considering this context, it is crucial to ensure end-to-end service quality that can fulfill users’ expectations. The user quality of experience (QoE) for multimedia streaming is tempered by numerous objective and subjective parameters; therefore, it is important to understand the relationships among them. In this paper, we thoroughly examine the impact of packet loss on user QoE in cases when multimedia streaming service is based on underlying User Datagram Protocol. The dependencies between the chosen objective and subjective parameters and the user QoE were examined in a real-life environment by conducting a survey with 602 test subjects who rated the quality of a 1-h documentary film (72 different test sequences were prepared for the rating process). Based on the obtained results, we ranked the objective parameters by their order of importance in relation to their impact on user QoE as follows: (1) total duration of packet loss occurrences (PLOs), i.e., quality distortions in a video; (2) number of PLOs; (3) packet loss rate; and (4) duration of a single PLO. We also demonstrated how the overall user experience can be redeemed, despite the perceived quality distortions, if the content is entertaining to the viewer. The user experience was also found to be influenced by the existence/non-existence of video subtitles.

This paper reports on the results of subjective testing of user Quality of Experience (QoE) for omnidirectional video (ODV) streaming quality. The test was conducted among 20 test subjects who watched three ODVs using a Head Mounted Display (HMD) system. The length of the videos was between two and three minutes. The first video was used for training purposes and contained no quality degradations. The quality of the other two ODVs was degraded by manipulating the resolution or by introducing different frame drop patterns. While watching the pre-prepared videos the subjects indicated if they noticed the changes in the quality and then rated it. After watching each video, the subjects completed a separate questionnaire, which evaluated their level of enjoyment and discomfort with the video. The results showed that the degradation of both objective parameters (video resolution and frame rate) impacted the subjects’ perception of quality; however, the impact was somewhat alleviated in ODV which contained dynamic scenes and fast camera movements.

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.

Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service.