A QoE Model for Mulsemedia TV in a Smart Home Environment

The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user's Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications

Real-Time QoE Monitoring System for Video Streaming Services with Adaptive Media Playout

Quality of Experience (QoE) of video streaming services has been attracting more and more attention recently. Therefore, in this work we designed and implemented a real-time QoE monitoring system for streaming services with Adaptive Media Playout (AMP), which was implemented into the VideoLAN Client (VLC) media player to dynamically adjust the playout rate of videos according to the buffer fullness of the client buffer. The QoE monitoring system reports the QoE of streaming services in real time so that network/content providers can monitor the qualities of their services and resolve troubles immediately whenever their subscribers encounter them. Several experiments including wired and wireless streaming were conducted to show the effectiveness of the implemented AMP and QoE monitoring system. Experimental results demonstrate that AMP significantly improves the QoE of streaming services according to the Mean Opinion Score (MOS) estimated by our developed program. Additionally, some challenging issues in wireless streaming have been easily identified using the developed QoE monitoring system