Quality utility modelling for multimedia applications for Android mobile devices

With the advances in mobile technologies, smart mobile computing devices have become increasingly affordable and powerful, leading to a significant growth in both the number of advanced mobile users and their bandwidth demands. Moreover multimedia streaming to these high-end mobile devices has become widespread. However, multimedia applications are known to be resource-hungry and in order to cope with this explosion of data traffic, operators have started deploying different, overlapping radio access network technologies. One important challenge in such a heterogeneous wireless environment is to ensure an Always Best Experience to the mobile user, anywhere and anytime. This paper proposes the Quality Utility, a realistic mapping function of the received bandwidth to user satisfaction for multimedia streaming applications. The Quality Utility is mapped to a Google Nexus One Android Mobile device and validated through objective and subjective tests

A machine learning resource allocation solution to improve video quality in remote education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented

Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results

Trade union strategies in the age of austerity: the Romanian public sector in comparative perspective

This article examines the impact of the economic crisis and its aftermath on collective bargaining, by comparing reactions to austerity policies of trade unions in healthcare and education in Romania. We develop an encompassing theoretical framework that links strategies used by trade unions with power resources, costs and union democracy. In a tight labour market generated by the massive emigration of doctors, unions in healthcare have successfully deployed their resources to advance their interests and obtain significant wage increases and better working conditions. We also show that in the aftermath of the crisis, healthcare trade unions have redefined their strategies and adopted a more militant stance based on a combination of local strikes, strike threats and temporary alliances with various stakeholders. By comparison, we find that unions in the education sector have adopted less effective strategies built around negotiations with governments combined with national-level militancy

Is Multimedia Multisensorial? - A Review of Mulsemedia Systems

© 2018 Copyright held by the owner/author(s). Mulsemedia - multiple sensorial media - makes possible the inclusion of layered sensory stimulation and interaction through multiple sensory channels. e recent upsurge in technology and wearables provides mulsemedia researchers a vehicle for potentially boundless choice. However, in order to build systems that integrate various senses, there are still some issues that need to be addressed. is review deals with mulsemedia topics remained insu ciently explored by previous work, with a focus on multi-multi (multiple media - multiple senses) perspective, where multiple types of media engage multiple senses. Moreover, it addresses the evolution of previously identi ed challenges in this area and formulates new exploration directions.This article was funded by the European Union’s Horizon 2020 Research and Innovation program under Grant Agreement no. 688503

A study of learning experience with a dash-based multimedia delivery system

In order to create an improved learning experience in variable network delivery conditions, multimedia content adjustment is performed when delivered over existing network environments. This paper introduces a study of user learning when multimedia-based study material is distributed at different quality levels in the context of the European Horizon2020 project NEWTON. This paper studies the learning experience with multimedia when employing an MPEG-DASH-based adaptive multimedia delivery in a real life subjective experiment with 88 Data Network students from two Irish and Slovak universities

A DASH-based mulsemedia adaptive delivery solution

European Union’s Horizon 2020 Research and Innovation programme via Grant Agreement no. 688503 for NEWTON (http://newtonproject.eu); China Postdoctoral Science Foundatio

Can multisensorial media improve learner experience?

In recent years, the emerging immersive technologies (e.g. Virtual/Augmented Reality, multisensorial media) bring brand-new multi-dimensional effects such as 3D vision, immersion, vibration, smell, airflow, etc. to gaming, video entertainment and other aspects of human life. This paper reports results from an European Horizon 2020 research project on the impact of multisensoral media (mulsemedia) on educational learner experience. A mulsemediaenhanced test-bed was developed to perform delivery of video content enhanced with haptic, olfaction and airflow effects. The results of the quality rating and questionnaires show significant improvements in terms of mulsemedia-enhanced teaching

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality