CONVINcE addresses the challenge of reducing the power consumption in IP-based video networks with an end-to-end approach, from the headend where contents are encoded and streamed to the terminals where they are consumed, embracing the Content Delivery Networks (CDN) and the core and access networks.
Partners’ efforts concentrated on architectures, hardware and software design, protocols and basic technologies in the devices. In parallel to these activities, the project ran transversal activities on “Software best practices & Eco-design” and “Power & QoE measurements”. The project also considered the use of new technologies in the form of Software Defined Networking (SDN) associated with Network Function Virtualization (NFV).
The CONVINcE project was built on three strong pillars (so-called work packages) investigating power saving in the headend, in the networks and in the terminals. In order to ensure an end-to-end approach for energy saving, another work package was fully dedicated to system architecture, power optimization & related business cases. Results coming from these four work packages provided inputs to a fifth one in charge of building demonstrators and developing new tools for QoE and power measurements. Finally, results of the project were disseminated and exploited through standardization, publication and demonstration activities.
Three architectural solutions were studied in the project: non-cloud-based architecture, edge-cloud based architecture and SDN/NFV based architecture. The goal was to compare energy consumption with these three approaches and then deduce recommendations. Based on this approach, the project answered fundamental questions regarding the end-to-end energy saving while guaranteeing to the end-user the same QoE with different architectures.
This video describes the shortly the project and shows the use cases that the project partners demonstrated.
Sensative scope
In the project, we developed the Yggio control panel for device management to manage WiFi routers in a multi-apartment building and using machine learning algorithms to continuously monitor and configure routers to ensure a high level of WiFi coverage and service throughout the building without congestion and interference between adjacent routers the same frequency and channels.
Project partners
16 partners including Ericsson, Sony, Orange, VTT and Lund University
Project duration: Sept 2014 – Sept 2017
Financing: EU (Celtic NEXT) through Vinnova
