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Recent studies have started to consider the HbbTV as the central hub to handle notifications regarding the smart home devices. The HbbTV integrates into the multimedia TV contents the notifications coming from various smart home devices such as the smartphone, video door-phone, sensors, washing machine, etc. Therefore, there is a need to evaluate the impact on-screen notifications would have on the user’s perceived Quality of Experience (QoE). In some cases, notifications may annoy the user and negatively impact the QoE perceived for the watched video contents. For these reasons, a subjective quality assessment is presented in order to evaluate the impact of onscreen notifications while users are watching TV video contents. Test participants were recorded while watching the video because, using the eye’s gaze direction, the attention of the participants towards notifications was measured.

The time lost looking for a free parking spot in a city impacts negatively not only on the mood of the drivers but also on the environment in terms of air quality and fuel consumption. The vehicle detection can be considered as the most important task in Smart Parking systems as it allows to automatically monitor the occupancy state of the parking spots in a city. In this paper, we implement and test a vehicle detection system based on a magnetometer sensor, which is part of a complete Smart Parking system under development at the University of Cagliari. After a preliminary analysis conducted to test the performance of the magnetometer, we conducted two specific experiments to investigate the suitability of the magnetometer as the mean to detect the presence of a vehicle in the parking spots. The first experiment, involving 15 different vehicles, has demonstrated that the magnetometer can be used to reliably detect the presence of a vehicle in a parking spot if it is placed under the front or rear axle of the vehicle. From the second experiment it resulted that, when considering 3 adjacent parking spots and only one magnetometer placed in the central spot, it is not possible to reliably detect the vehicles parked on the adjacent spots. Therefore, one magnetometer for each considered parking spot is needed.

As known from everyday contexts of multimedia usage, suddenly occurring quality impairments are capable of causing strong negative emotions in human users. This is particularly the case if the displayed content is highly relevant to current motives and behavioral goals. The present study investigated the effects of visual degradations on quality perception and emotional state of participants who were exposed to a series of short video clips. After each video playback, participants had to decide whether a certain event happened in the video. For data collection, subjective measures of quality and emotion were complemented by behavioral measures derived from capturing participants’ spontaneous facial expressions. For data analysis, two general approaches were combined: First, a multivariate analysis of variance approach allowed to examine the effects of visual degradation factors on perceived quality and subjective emotional dimensions. It mainly revealed that perceived quality and emotional valence were both sensitive to degradation intensity, whereas the impact of degradation length was limited when task-relevant video content had already been obscured. Second, using a machine learning approach, an automatic Video Quality of Experience (VQoE) prediction system based on the recorded facial expressions was derived, demonstrating a strong correlation between facial expressions and perceived quality. Hereby, estimates of VQoE might be delivered in an objective, continuous and concealed manner, thus diminishing any further need for subjective self-reports.

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.

In this paper we investigate on the potentials to implicitly estimate the Quality of Experience (QoE) of a user of video streaming services by acquiring a video of her face and monitoring her facial expression and gaze direction. To this, we conducted a crowdsourcing test in which participants were asked to watch and rate the quality when watching 20 videos subject to different impairments, while their face was recorded with their PC’s webcam. The following features were then considered: the Action Units (AU) that represent the facial expression, and the position of the eyes’ pupil. These features were then used, together with the respective QoE values provided by the participants, to train three machine learning classifiers, namely, Support Vector Machine with quadratic kernel, RUSBoost trees and bagged trees. We considered two prediction models: only the AU features are considered or together with the position of the eyes’ pupils. The RUSBoost trees achieved the best results in terms of accuracy, sensitivity and area under the curve scores. In particular, when all the features were considered, the achieved accuracy is of 44.7%, 59.4% and 75.3% when using the 5-level, 3level and 2-level quality scales, respectively. Whereas these results are not satisfactory yet, these represent a promising basis.

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.