Adaptive Streaming: A subjective catalog to assess the performance of objective QoE metrics

Scalable streaming has emerged as a feasible solution to resolve users' heterogeneity problems. SVC is the technology that has served as the definitive impulse for the growth of streaming adaptive systems. Systems seek to improve layer switching efficiency from the network point of view but, with increasing importance, without jeopardizing user perceived video quality, i.e., QoE. We have performed extensive subjective experiments to corroborate the preference towards adaptive systems when compared to traditional non-adaptive systems. The resulting subjective scores are correlated with most relevant Full Reference (FR) objective metrics. We obtain an exponential relationship between human decisions and the same decisions expressed as a difference of objective metrics. A strong correlation with subjective scores validates objective metrics to be used as aid in the adaptive decision taking algorithms to improve overall systems performance. Results show that, among the evaluated objective metrics, PSNR is the metric that provide worse results in terms of reproducing the human decision

Quality of experience aware adaptive hypermedia system

The research reported in this thesis proposes, designs and tests a novel Quality of Experience Layer (QoE-layer) for the classic Adaptive Hypermedia Systems (AHS) architecture. Its goal is to improve the end-user perceived Quality of Service in different operational environments suitable for residential users. While the AHS’ main role of delivering personalised content is not altered, its functionality and performance is improved and thus the user satisfaction with the service provided. The QoE Layer takes into account multiple factors that affect Quality of Experience (QoE), such as Web components and network connection. It uses a novel Perceived Performance Model that takes into consideration a variety of performance metrics, in order to learn about the Web user operational environment characteristics, about changes in network connection and the consequences of these changes on the user’s quality of experience. This model also considers the user’s subjective opinion about his/her QoE, increasing its effectiveness and suggests strategies for tailoring Web content in order to improve QoE. The user related information is modelled using a stereotype-based technique that makes use of probability and distribution theory. The QoE-Layer has been assessed through both simulations and qualitative evaluation in the educational area (mainly distance learning), when users interact with the system in a low bit rate operational environment. The simulations have assessed “learning” and “adaptability” behaviour of the proposed layer in different and variable home connections when a learning task is performed. The correctness of Perceived Performance Model (PPM) suggestions, access time of the learning process and quantity of transmitted data were analysed. The results show that the QoE layer significantly improves the performance in terms of the access time of the learning process with a reduction in the quantity of data sent by using image compression and/or elimination. A visual quality assessment confirmed that this image quality reduction does not significantly affect the viewers’ perceived quality that was close to “good” perceptual level. For qualitative evaluation the QoE layer has been deployed on the open-source AHA! system. The goal of this evaluation was to compare the learning outcome, system usability and user satisfaction when AHA! and QoE-ware AHA systems were used. The assessment was performed in terms of learner achievement, learning performance and usability assessment. The results indicate that QoE-aware AHA system did not affect the learning outcome (the students have similar-learning achievements) but the learning performance was improved in terms of study time. Most significantly, QoE-aware AHA provides an important improvement in system usability as indicated by users’ opinion about their satisfaction related to QoE

Energy Efficient Scheduling for Loss Tolerant IoT Applications with Uninformed Transmitter

In this work we investigate energy efficient packet scheduling problem for the loss tolerant applications. We consider slow fading channel for a point to point connection with no channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters and peak power constraints, we formulate an optimization problem to minimize the average transmit energy for the user equipment (UE). The optimization problem is not convex and we use stochastic optimization technique to solve the problem. The numerical results quantify the effect of different system parameters on average transmit power and show significant power savings for the loss tolerant applications.Comment: Published in ICC 201

Virtual transcendence experiences: Exploring technical and design challenges in multi-sensory environments

In this paper 1, we introduce the concept of Virtual Transcendence Experience (VTE) as a response to the interactions of several users sharing several immersive experiences through different media channels. For that, we review the current body of knowledge that has led to the development of a VTE system. This is followed by a discussion of current technical and design challenges that could support the implementation of this concept. This discussion has informed the VTE framework (VTEf), which integrates different layers of experiences, including the role of each user and the technical challenges involved. We conclude this paper with suggestions for two scenarios and recommendations for the implementation of a system that could support VTEs