QoE-Driven, Energy-Aware Video Adaptation in 5G Networks: The SELFNET Self-Optimisation Use Case

James Nightingale, Qi Wang, Jose M. Alcaraz Calero, Enrique Chirivella-Perez, Marian Ulbricht, Jesús A. Alonso-López, Ricardo Preto, Tiago Batista, Tiago Teixeira, Maria Joao Barros and Christiane Reinsch

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 1, 7829305

Abstract: 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:1:p:7829305

DOI: 10.1155/2016/7829305

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