It is well-known that, without access to application-layer parameters controlled by Over-The-Top (OTT) providers, Mobile Network Operators (MNOs) struggle to accurately predict customers’ Quality of Experience (QoE). While some previous proposals have suggested interaction between OTTs and MNOs, they have faced challenges in terms of practical implementation and limited application scenarios. This work aims to advance these solutions with two key contributions. First, following the Open Radio Access Network (O-RAN) architecture, we propose adding components that integrate a machine learning (ML)-based QoE prediction model, deployed by the MNO, into the O-RAN system. By establishing specific data-sharing interfaces between OTTs and MNOs, our approach helps MNOs overcome the limitations in updating their quality prediction modules. Second, we present a network-aware, ML-driven QoE prediction model that captures the relationship between the resulting QoE and various network parameters, such as signal-to-interference-noise ratio (SINR), channel quality indicator (CQI), network resource blocks (RBs), throughput, and device mobility. Among seven considered ML regressors, the Gradient Boosting (GB) achieved the highest QoE prediction performance in terms of R 2 (0.906) and RMSE (0.259).

Human Digital Twins (HDTs) are increasingly adopted across various domains, yet their application to network and service management remains limited. Nevertheless, HDTs offer significant potential for optimizing service configurations based on human behavior, preferences, and profiles. In this paper, we analyze the role of HDTs in network and service management, identifying key functionalities such as collaborative learning for user modelling, Quality of Experience (QoE) and emotion prediction, application personalization, and behavioral forecasting for network “what-if” analysis. We propose an architectural framework designed to monitor user status and generate a corresponding digital replica that interacts with other network components to enhance service delivery. Our solution integrates collaborative learning for QoE modelling and applies it to service optimization. By aggregating user data from multiple HDTs, the approach improves prediction accuracy and resource optimization. Extensive performance evaluations demonstrate that the proposed collaborative HDT framework enhances the final utility function that considers perceived quality and resource usage by 27% compared to non-collaborative methods.

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.

Accurate models are necessary for the continuous estimation of the Quality of Experience (QoE), which is crucial for delivering successful multimedia services to end-users. These models are developed from subjective test data, which very often provide only specific aspects of the experience, i.e., a partial view (PV). Each PV conveys a relationship between a specific set of influence factors and the perceived QoE, limiting the applicability of the derived model to other application scenarios not considered in the initial subjective tests. To extend the applicability of the developed models, this paper introduces a multi-view (MV) learning framework that enhances QoE prediction by integrating complementary information from multiple perspectives obtained from different subjective tests. We leverage a fully connected deep neural network with two initially independent branches and an intermediate fusion layer to combine insights from separate feature sets, improving predictive accuracy while preserving data privacy. Our model is trained on a synthetic data set derived from the TID2008 image database, ensuring a controlled yet representative evaluation environment. On the one hand, the results demonstrate that the MV technique outperforms all PV configurations. On the other hand, the MV approach achieves QoE estimation performance comparable to the single-view (SV) model, in which one single branch analyzes the full set of impact factors. In particular, the largest performance gain (6.15% – 142.69%) across most evaluation metrics occurred when the input data set is equally divided between the two separate views.

Point cloud quality assessment (PCQA) is a critical research area focused on evaluating the perceptual Quality of Experience (QoE) of point clouds to enhance visual experiences of immersive multimedia applications for end users. To prevent the complex computations on 3D data applied by model-based methods, projection-based models have been developed to estimate the QoE by analysing 2D projection views of the point cloud. In this paper, we propose a novel projection-based No-Reference (NR) PCQA method, called Multi-View Adaptive Weighting Point Cloud Quality Assessment (MVAW-PCQA), to predict the QoE of distorted point clouds using six 2D projection views as the input of a convolutional neural network (CNN) architecture. First, multi-view involves independently extracting features from multiple projection views of a point cloud, guaranteeing view-specific features are learned without prematurely mixing spatial information, and preserving the unique contributions of each projection view to the final quality prediction. Then, an adaptive weighting fusion mechanism combines the features extracted from the different projection views by learning their relative importance. This design enables the model to focus on the most informative projections for predicting the point cloud quality. The experimental results demonstrate that our method outperforms state-of-the-art NR-PCQA methods on the SJTU-PCQA dataset in terms of root mean square error (RMSE) and correlation coefficients (Pearson, Spearman, and Kendall), while adopting a lightweight design with a reasonable number of parameters for the trained neural network.

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.

Multi-view (MV) learning is a machine learning technique for improving generalization efficiency by learning from different feature subsets derived from multiple sources. We believe this approach can help in Quality of Experience (QoE) modelling by integrating knowledge from different datasets generated by subjective tests conducted for the same or similar applications considering different QoE Influence Factors (IFs). To investigate this subject, in this paper, we present the experiments conducted starting from a complete dataset related to Web browsing sessions that has been artificially divided into two distinct subsets (views). The proposed MV learning approach implements a data fusion technique to integrate extracted features from different views into a unified feature space. To achieve a complete experiment on the entire problem space, all possible combinations of IFs (features) in two distinct partial views (PVs) are considered and trained in the MV approach; the full view (FV) approach, which utilizes the complete dataset, is also considered for performance comparison. Experimental results show the QoE estimation performance achieved by the MV (0.69) is comparable with that of the FV (0.72), although the 2 single views were used for training in the MV case. Moreover, the performance enhancement achieved by the MV compared with the PV is most noticeable when a lower number of features is used to train the models.

This paper proposes NetRate, an Android system monitoring application that can measure and collect network and energy information from Android devices and enables end users to rate the Quality of Experience (QoE) of selected applications. Moreover, NetRate includes a VPN service to limit the network capabilities of the end device to simulate constrained network conditions. NetRate supports the conduction of QoE research studies in the laboratory and the field by collecting different kinds of data from the end device, assisting in setting test conditions (e.g., with the VPN), and collecting subjective feedback.

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.

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction.

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.

The Metaverse provides a novel experience to the user, by opening the doors to social-based multiuser environments merging physical reality with digital virtuality. In this paper, we present an initial analysis of the Quality of Experience (QoE) in the Metaverse. We first consider traditional influence factors (human, system, and context). Then, we introduce the social and economic dimensions of the Metaverse as additional factors to be considered for QoE assessment. Finally, we discuss what QoE assessment methods can be more suitable for Metaverse applications, with a particular focus on implicit assessment methods (e.g., physiological, human cognitive, affective behaviour).

Nowadays, Internet of Things (IoT) applications are widespread in different scenarios, such as industry, mobility, healthcare, and agriculture. A relevant share of the credit is due to the Blockchain technology, which provides important features to IoT services, such as decentralized validation of transactions as well as immutability and traceability of the transactions. In this paper, we propose a Blockchain-based data notarization system for mobility services. First, we present an IoT-based crowd monitoring system aimed at counting the number of people in a specific area and providing information regarding people mobility (i.e., how people move within the city) and dwell time (i.e., the time people stayed at specific places). Then, we discuss our proposed data notarization system focused on ensuring data integrity and immutability of the mobility data collected by the crowd monitoring system, regardless of the used Blockchain. Finally, we provide experimental results regarding people mobility data collected during a literary event as well as an implementation of the proposed data notarization system using the EthernaZero blockchain.

Coast erosion is a process that degrades a coastal profile and is mainly due to natural factors (e.g., related to climate change) and overcrowding (e.g., urbanization and massive tourism). While the first cause can be considered as a slow process, the growing presence of humans is leading to rapid aging of coasts. The Mediterranean Sea authorities are focusing on the necessity for a systematic and comprehensive approach to the management of littoral areas. In this context, Italy is searching for a promising solution to safeguard coasts but, at the same time, to manage in an intelligent and “green” way the big amount of tourists. Research communities all over the world indicated the Internet of Things (IoT) as a valid technology to develop solutions in order to try solving or mitigating the coastal erosion problem. IoT-based techniques allow to manage heterogeneous and massive data for real-time monitoring and decision making and can be used for coastal environment and crowd level monitoring. This article presents a monitoring system based on the Social IoT (SIoT), a new paradigm that defines a network where every node is an object capable of establishing the social relationships with other things in an autonomous way according to specific rules. Thanks to social relationships, all involved devices in the monitoring system (i.e., sensors, cameras, and smartphones) are able to collect and exchange information. The proposed system, developed and installed in Cagliari (Italy), is able to evaluate the occupational state of a beach considering environmental and crowding data collected by devices and feedback sent by users.

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber. We aim to provide the research community with an experimental tool for the design and evaluation of the new Quality of Experience (QoE) management and monitoring procedures for video streaming. To this aim, the main functionalities of Timber include: i) an SDN application for taking QoE-aware management decisions; ii) an SDN controller to monitor the network's QoS (Quality of Service) and implement network management actions, such as network slicing and Multiprotocol Label Switching (MPLS) based prioritization operations; iii) a complete video streaming application including a multimedia server and a DASH-based client video player; iv) a user-end probe at the client video player to monitor QoE-related video application parameters, which are stored in a database that can be accessed by the SDN application; v) data analysis tools, which enable easy data visualization of measured QoS and QoE metrics as well as execution of statistical analysis of experimental results. In this article, we introduce and describe the main characteristics and functionalities of Timber as well as the implementation details. Finally, we provide experimental results of a video streaming scenario to demonstrate the capability of Timber to implement and test QoE-aware management approaches.

This article investigates the possibility to estimate the perceived Quality of Experience (QoE) automatically and unobtrusively by analyzing the face of the consumer of video streaming services, from which facial expression and gaze direction are extracted. If effective, this would be a valuable tool for the monitoring of personal QoE during video streaming services without asking the user to provide feedback, with great advantages for service management. Additionally, this would eliminate the bias of subjective tests and would avoid bothering the viewers with questions to collect opinions and feedback. The performed analysis relies on two different experiments: i) a crowdsourcing test, where the videos are subject to impairments caused by long initial delays and re-buffering events; ii) a laboratory test, where the videos are affected by blurring effects. The facial Action Units (AU) that represent the contractions of specific facial muscles together with the position of the eyes' pupils are extracted to identify the correlation between perceived quality and facial expressions. An SVM with a quadratic kernel and a k-NN classifier have been tested to predict the QoE from these features. These have also been combined with measured application-level parameters to improve the quality prediction. From the performed experiments, it results that the best performance is obtained with the k-NN classifier by combining all the described features and after training it with both the datasets, with a prediction accuracy as high as 93.9% outperforming the state of the art achievements.

The physical context, i.e., the characteristics of location and space where the multimedia service is consumed, may strongly influence the overall perceived Quality of Experience (QoE). In this paper, we investigate the effects of ambient illumination and noise on two multimedia consumption scenarios: watching a video on TV and reading a comic strip on tablet. To this aim, we organized an experiment considering different combinations of ambient illumination and introducing a disturbing noise. Then, we conducted a subjective quality assessment involving 20 people, who were asked to rate the perceived QoE using the 5-level Absolute Category Rating (ACR) quality scale and to express their emotions completing the Self-Assessment Manikin (SAM) questionnaire. The impact of illumination and noise on ACR ratings and SAM scores is evaluated computing the Multivariate Analysis of Variance (MANOVA). Finally, a QoE prediction model based on illumination and noise context factors is presented.

Recent studies implemented collaboration between Over-The-Top (OTT) service and Internet Service Provider (ISP) concerning information exchange between the providers. This information is used to accurately predict the delivered Quality of Experience (QoE) and decide the correcting network management actions. In this scenario, we aim to investigate the impact of the frequency of information exchange between OTT and ISP on end users' QoE and network resource utilization. Firstly, we propose our information-centric QoE-aware collaborative service management approach by defining the type of information to be acquired and exchanged between OTT and ISP. Secondly, we present our platform based on the Software-Defined Networking (SDN) paradigm, which we used to conduct the experiments. Finally, we conduct experiment results that compare the proposed collaborative approach with the case of no collaboration when the sampling interval of information exchange varies between 2 s and 32 s. The experiment results show that a higher frequency of information exchange may result in better network reliability and delivered QoE, but a frequency higher than 1/4 Hz may not further improve the delivered QoE.

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber. It is aimed at providing the research community with a tool for experimenting new Quality of Experience (QoE) management procedures and tools in multimedia service delivery. Timber is developed on the top of Mininet SDN emulator and Ryu SDN controller, which provides the major functionalities of the traffic engineering abstractions in SDN environment. Moreover, the platform provides an actual complete video streaming application including the implementation of the server side and client side probes for QoE measurements which have functionalities to store the quality measurements into the cloud database accessible to the SDN controller application. In this paper, we first discuss the general architecture and framework of Timber. Secondly, we provide the implementation details and major functionalities of the platform. Thirdly, we provide experimental results to highlight the major functionalities of Timber by 4 different scenarios which include traffic shaping through DiffServ and dynamic resource allocation by queuing strategies.

This paper investigates the applicability of QoE management on Smart City services. First, we analyze how quality management is implemented for traditional public services (services for which the presence of ICT technologies is limited or not necessary). We then propose a potential framework for the QoE management in Smart City services (traditional public services supported by an important, but not essential, presence of ICT systems), which is based on ICT systems for QoE prediction and management. Finally, we highlight the challenges to be addressed in the near future.

It is a matter of fact that quality of experience (QoE) has become one of the key factors determining whether a new multimedia service will be successfully accepted by the final users. Accordingly, several QoE models have been developed with the aim of capturing the perception of the user by considering as many influencing factors as possible. However, when it comes to adopting these models in the management of the services and networks, it frequently happens that no single provider has access to all of the tools to either measure all influencing factors parameters or control over the delivered quality. In particular, it often happens to the over-the-top (OTT) and Internet service providers (ISPs), which act with complementary roles in the service delivery over the Internet. On the basis of this consideration, in this article we first highlight the importance of a possible OTT-ISP collaboration for a joint service management in terms of technical and economic aspects. Then we propose a general reference architecture for a possible collaboration and information exchange among them. Finally, we define three different approaches, namely joint venture, customer lifetime value based, and QoE fairness based. The first aims to maximize the revenue by providing better QoE to customers paying more. The second aims to maximize the profit by providing better QoE to the most profitable customers (MPCs). The third aims to maximize QoE fairness among all customers. Finally, we conduct simulations to compare the three approaches in terms of QoE provided to the users, profit generated for the providers, and QoE fairness.

In this work, we propose a QoE-aware collaboration approach between Over-The-Top providers (OTT) and Internet Service Providers (ISP) based on the maximization of the profit by considering the user churn of Most Profitable Customers (MPCs), which are classified in terms of the Customer Lifetime Value (CLV). The contribution of this work is multifold. Firstly, we investigate the different perspectives of ISPs and OTTs regarding QoE management and why they should collaborate. Secondly, we investigate the current ongoing collaboration scenarios in the multimedia industry. Thirdly, we propose the QoE-aware collaboration framework based on the CLV, which includes the interfaces for information sharing between OTTs and ISPs and the use of Content Delivery Networks (CDN) and surrogate servers. Finally, we provide simulation results aiming at demonstrating the higher profit is achieved when collaboration is introduced, by engaging more MPCs with respect to current solutions.

The objective of this work is the investigation of a possible collaboration between Over-The-Top (OTTs) service providers and Internet Service Providers (ISPs), which is centered around the Quality of Experience (QoE). Initially, we define a reference architecture with the required modules and interfaces for the interaction between the two providers. Then, we focus on the modeling of the revenue, whose maximization drives the collaboration. It is considered as depending on the user churn, which in turn is affected by the QoE and is modeled using the Sigmoid function. We illustrate simulation results based on our proposed collaboration approach which highlights how the proposed strategy increases the revenue generation and QoE for both players hence providing a ground for ISP to join the loop of revenue generation between OTT and users.

In this paper, a Quality of Experience (QoE)-aware Smart Home Energy Management (SHEM) system is proposed. Firstly, a survey has been conducted on 64 people to investigate the degree of satisfaction perceived when the starting time of appliances was postponed or anticipated with respect to the preferred time. Secondly, the results were clustered in different profiles using the k-means algorithm to control appliances' working time according to the detected user profile. Thirdly, a SHEM system is run that relies on two algorithms: the QoE-aware Cost Saving Appliance Scheduling (Q-CSAS) and the QoE-aware Renewable Source Power Allocation (Q-RSPA). The former is aimed at scheduling controllable loads based on users' profile preferences and Time-of-Use (TOU) electricity prices, thus taking into account the level of annoyance perceived when a task is postponed or anticipated. The latter re-allocates the starting time of appliances whenever a surplus of energy has been made available by Renewable Energy Sources (RES). This re-allocation takes place using a distributed max-consensus negotiation algorithm. The objective is that of scheduling the appliances starting time so that a trade-off between cost saving and annoyance perceived is achieved. As demonstrated by simulation results, the two algorithms ensure a cost saving that goes from 19% to 84% depending on the presence of RES, with a resulting average annoyance factor value of 1.01 to 1.03.

HTTP adaptive streaming technology is now widely adopted in multimedia services because of its ability to provide adaptation to the streaming context, especially characteristics of end-user devices and dynamic network conditions. There are various studies targeting the evaluation of the Quality of Experience (QoE) in this framework. However, none has considered the scenario of the user changing the viewing device during the streaming session, which is the objective of this paper. It provides the following major contributions: definition of the multi-device streaming session scenario; the implementation of a realistic testing case; the execution of subjective tests involving 28 people; and the detailed analysis of the influence of the devices' switching events.

We propose a source-rate control scheme for streaming video over a wireless channel. The scheme is designed to maximize the quality of the decoded video as perceived at the user-side by resorting on a reduced-reference video-quality estimation approach. The advantage is that the measured video quality, which drives the rate-control algorithm, is obtained after channel errors and error concealment. The rate control algorithm works adjusting the rate on a per-window basis to compensate low-throughput periods with high-throughput periods so as to avoid the “saw” effect that is typically observed in frame-based rate control.