Monitoring and control of QoE in large scale media distribution architectures
Project Status: Finished
Start Date: October 2016
End Date: December 2018
Budget (total): 5221 K€
Effort: 58.5 PY
Name: Antonio Cuadra-Sanchez
INDRA Sistemas, Spain
Orange Polska S.A., Poland
Mint Media sp. z o.o., Poland
National Institute of Telecommunications, Poland
Instituto de Telecomunicações, Portugal
Nokia Spain SA, Spain
The multimedia content distribution volumes have continued to increase for many years and will continue to do so for a many more. Services such as Netflix have already become a major contributor to Internet traffic volumes in the US, and the same patterns can be predicted for European networks. Internet media services are provided through large scale distribution architectures to often millions of clients, with high demands on QoS/QoE and strict performance bounds. These systems, therefore, face similar design and operation challenges as many other complex engineering systems, for example, the challenge of monitoring and analyzing service performance for millions of clients in real-time in order to find and repair performance problems and system failures to avoid service degradations.
In the MONALIS project we will address these challenges. The project consortium consists of partners from five countries, where the industry partners span over basically the whole eco-system of media distribution and where the academic partners guarantee high scientific quality. The MONALIS consortium includes content providers (Magine), network providers (Alcatel-Lucent, Ericsson AB), telecom operators (TeliaSonera, Orange Poland), network monitoring tool providers (Procera Networks, PERCEVIO), video encoding solutions (ATEME), universities (UPEC, Lund University, Universidad Politecnica de Madrid, Institut Mines-Telecom), research institutes (Acreo, SICS, Instituto Telecomunicações, National Institute of Telecommunication), and SMEs focused on multimedia solutions (Alkit Communication, VIOTECH, Mint Media).
The MONALIS project will provide monitoring techniques and tools using Big Data analytics for QoE evaluation and analysis relevant for overall customer satisfaction, provide measurement platforms in a Big Data environment making data available from all network levels from low level packet information up to data about end user experience, provide an experimental evaluation of an over-the-top video-on-demand distribution system with QoE monitoring and exchange of QoE-relevant data between actors in the delivery chain, and provide new business models that take advantage of big data frameworks.