QoE-driven, energy-aware video adaptation in 5G networks: the SELFNET self-optimisation use case

James Nightingale, Qi Wang, Jose Maria Alcarez Calero, Enrique Chirvella-Perez, Marian Ulbricht, Jess A. Alonso-López, Ricardo Preto, Tiago Batista, Tiago Teixeira, Maria Joao Barros, Christiane Reinsch

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Sharp increase of video traffic is expected to account for the majority of traffic in future 5G networks. This paper introduces the SELFNET 5G project and describes the video streaming use case that will be used to demonstrate the self-optimising capabilities of SELFNET's autonomic network management framework. SELFNET's framework will provide an advanced self-organizing network (SON) underpinned by seamless integration of Software Defined Networking (SDN), Network Function Virtualization (NFV), and network intelligence. The self-optimisation video streaming use case is going beyond traditional quality of service approaches to network management. A set of monitoring and analysis components will facilitate a user-oriented, quality of experience (QoE) and energy-aware approach. Firstly, novel SON-Sensors will monitor both traditional network state metrics and new video and energy related metrics. The combination of these low level metrics provides highly innovative health of network (HoN) composite metrics. HoN composite metrics are processed via autonomous decisions not only maintaining but also proactively optimising users' video QoE while minimising the end-to-end energy consumption of the 5G network. This contribution provided a detailed technical overview of this ambitious use case.

Original languageEnglish
Article number7829305
Number of pages15
JournalInternational Journal of Distributed Sensor Networks
Publication statusPublished - 1 Jan 2016


  • energy-aware video adaptation
  • 5G networks


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