This paper presents the Music Event Emotion Tracking (MEET) Metaverse, which is one of the demo results produced within the FUN-Media project. The MEET Metaverse is a virtual disco where multiple users can join together through their avatars to enjoy musical events. The peculiar characteristic of the MEET Metaverse is that the emotions of participants are inferred from their facial expressions and speech, and are used to select the next song to be played in the disco based on the average emotional state of participants. Moreover, avatars’ facial poses are updated based on participants’ emotions, and realistic avatar animations are reproduced using a combination of motion retargeting and high-fidelity appearance modeling.

This paper presents the results of a subjective assessment investigating the impact of multiple factors (network, avatar, and player role) on the perceived Quality of Experience (QoE) in a multi-user collaborative virtual reality (VR) game. Forty test participants collaborated in pairs to complete a cooking VR game, wearing a Meta Quest Pro headset, under variable network conditions (no impairments or delayed network traffic), using diverse types of avatars (cartoon-style and humanoid), and interpreting different roles (teacher and student). A humanoid custom avatar has been implemented that replicates the user’s facial expressions and body movements to investigate whether the introduction of non-verbal emotional communication within a VR environment influences the perceived user experience. Quality-and emotion-related subjective metrics were rated by test participants at the end of each test session, and the computed results show that the cartoon-like avatar, being lightweight, provides the highest perceived QoE even when the network was impaired. On the other hand, while the QoE using the humanoid avatar lowers with the introduction of network distortions (because of the larger amount of data required to replicate the facial expressions), the ability to see the facial expressions of the partner prevents a greater reduction of user experience due to the network issues.

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

The increasing demand for quality from multimedia service users is very often addressed by adding more resources (bandwidth and processing power). However, not always does this approach bring an improvement in the perceived quality, whereas it frequently implies an increase in energy consumption (and subsequent higher greenhouse gas emissions). Accordingly, in this paper, we propose a solution to dynamically allocate resources in a 5G heterogeneous network scenario, which aims to identify a trade-off between the overall QoE perceived by the users served by the network when consuming video content and the overall network energy consumption. We considered three types of devices (TV, laptop, and smartphone) for which appropriate QoE and energy consumption models are defined. Extensive simulations have been performed by assigning different levels of importance to QoE and energy. The achieved results show that the network energy consumption can be more than halved by keeping satisfactory QoE. This is particularly true for smartphone users, whereas TV and laptop users have the freedom to choose based on their sensitivity towards sustainability.

The utilization of user’s facial- and speech-related features for the estimation of the Quality of Experience (QoE) of multimedia services is still underinvestigated despite its potential. Currently, only the use of either facial or speech features individually has been proposed, and relevant limited experiments have been performed. To advance in this respect, in this study, we focused on WebRTC-based videoconferencing, where it is often possible to capture both the facial expressions and vocal speech characteristics of the users. First, we performed thorough statistical analysis to identify the most significant facial- and speech-related features for QoE estimation, which we extracted from the participants’ audio-video data collected during a subjective assessment. Second, we trained individual QoE estimation machine learning-based models on the separated facial and speech datasets. Finally, we employed data fusion techniques to combine the facial and speech datasets into a single dataset to enhance the QoE estimation performance due to the integrated knowledge provided by the fusion of facial and speech features. The obtained results demonstrate that the data fusion technique based on the Improved Centered Kernel Alignment (ICKA) allows for reaching a mean QoE estimation accuracy of 0.93, whereas the values of 0.78 and 0.86 are reached when using only facial or speech features, respectively.

The increasing use of ICT has raised concerns about its negative impact on energy consumption and$CO_{2}$emissions. To address this issue, there is a need to better understand the trade-off between Quality of Experience (QoE) and sustainable video streaming services. In this study, we designed and conducted a subjective assessment to investigate the impact of video resolution, different types of luminance, and different end devices on the QoE and energy consumption of video streaming services. Then, we applied statistical models (Analysis of Variance and t-test) to subjective data to find out what factors influence the QoE the most and consume more energy. The obtained results suggest that under specific conditions (e.g., dark or bright ambient, low device backlight luminance, small-screen device) the users could be encouraged towards a trade-off between acceptable QoE and sustainable (green) choices because spending more energy (e.g., streaming higher-quality video) would not provide noticeable QoE enhancement.

WebRTC-based applications allow for real-time communications that are subject to network impairments affecting the end user's Quality of Experience (QoE). In this paper, we conducted subjective tests involving 20 people to investigate the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. A dedicated system was implemented to introduce controlled network impairments (delay, jitter, and packet loss) to impair the communication between the parties. In addition, test participants had to rate the perceived QoE for the audio, the video, and the overall service, as well as the three emotional dimensions, i.e., valence, arousal, and dominance. Extensive results were obtained regarding the impact of the network impairments on the multimedia quality, the emotional dimensions, and the communication feasibility.