Antonio Cuadra Sánchez

Predicting quality of experience in multimedia streaming

Measuring and predicting the users Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the users feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the systems dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine Learning classification algorithms, trained on subjective test data. These models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment. Providing high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system.

Quality of Experience Models for Multimedia Streaming

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience QoE is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

Estimations and Remedies for Quality of Experience in Multimedia Streaming

Managing multimedia network services in a User-centric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality of Experience (QoE) is such a metric, which captures many different aspects that compose the perception of quality. The drawback of using QoE is that due to its subjectiveness, accurate measurement necessitates execution of cumbersome subjective studies. In this work we propose a method that uses Machine Learning techniques to build QoE prediction models based on limited subjective data. Using those models we have developed an algorithm that generates the remedies for improving the QoE of observed multimedia stream. Selecting the optimal remedy is done by comparing the costs in resources associated to each of them. Coupling the QoE estimation and calculation of remedies produces a tool for effective implementation of a User-centric management loop for multimedia streaming services.

User-driven context aware creation and execution of home care applications

In the Internet world, the tags Web 2.0 and user centricity have been used in recent times to reflect the democratization of the Web where end users are no longer passive entities and have assumed a key role in which they create content themselves. The evolution of this paradigm in the Internet has even given birth to tools like Microsoft Popfly or Yahoo Pipes, thanks to which end users with no specific computer skills are able to create their own highly personalized services. In an environment like home care, where each user has very specific individualized needs, the application of this new paradigm seems very promising, and to promote that philosophy porting, this paper presents a system to allow each individual and his/her carers to build, execute, and manage the specific personalized monitoring, comfort, automation, and safety applications required. Additionally, thanks to built-in, context awareness capabilities, the applications created are automatically adapted to the context of the users inside the proposed system, eliminating the need for frequent human–machine interaction which is of the uttermost importance for impaired users.

English Learning Based on Edutainment

A novel learning tool based on the new paradigm well-known as Edutainment using mobile technologies is deployed. The developed tool moves the issue of education to the context of entertainment. This paradigm ensures that education is seen as an activity that provides entertainment and excitement pleasure. This characteristic gives to lifelong learning an added value. Considering that the mobile-phone has had a boom in the last years and that a lot of people already have mobile telephones (approximately 2.5 billions), we have developed a system that makes lifelong learning something natural and effective for education in all fields of development. This obeys to that the new information technology can help us to achieve our perspectives of growth. This system provides to user with a permanent learning in anytime, anywhere and any device (PDAs, cell-phone, NDS, etc.). The English learning was enabled by the use of mobile phones and allowed us to detect some features that benefit learning in these contexts: exploring english independently (2) learning english through collaboration and team work (through Karaoke); (3) learning english in a societal and humanistic environment (4) learning english in authentic real life situations (5) learning english easily and efficiently (6) learning english (7) learning English fun and enjoyable. This indicates that english education could benefit form utilizing these new technological tools.

Puebla in the palm of your hands

An interactive tourist tool with integrated maps navigation of Puebla is deployed. Mapping and navigation to all local points of interest such as hotels, restaurants, attractions, etc., are contained in our mobile tool. This is a novel mobile tool that support the efficient tourists in planning their stay in the Puebla City showing user a set of tourist sites and services, both useful for tourists, of which user chooses those who want to know. From this set of selected sites our application determines the shortest path using the traveling salesman algorithm. Besides, emergency services information about all local points is available. Generation of custom path is integrated, allowing user to have a google maps with all places to visit with a true an accurate location. Finally, our application allows the user to display maps of tourist sites in real time without the GPS service, instead we use GPRS via the WAP with the Google Maps API. This dramatically reduces costs benefiting tourism in Puebla.

Adjunct Enterprise Professors in the European Higher Education Area

The PAVEs Program (Profesores Asociados Vinculados a Empresa - Adjunct Enterprise Professors) of the University of Valladolid allows enterprises to offer a set of optional subjects dealing with the technological knowledge necessary for their operation. These subjects are not only very attractive for the students, as they are given the chance to get an insight of the world that will host them as employees in the future, but also could be studied to extract lessons about the future European Higher Education Area (EHEA). This work presents the PAVEs program, the lessons learned that could be applied to the plan adaptation to the EHEA, and how the PAVEs subjects could be better integrated with ordinary subjects in the upcoming EHEA.