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.
Highlights
Traffic generated by video applications has increasingly dominated 3G/4G mobile networks, placing great strain on network capacity
This paper has introduced the SELFNET framework for the autonomous management of 5G network infrastructures and described the self-optimisation use case that will be used to validate the SELFNET framework, demonstrate its ability to meet 5G Key Performance Indicators (KPIs)’s, and deliver new, user-oriented services
The use case employs a series of ultrahigh definition video streaming scenarios to illustrate the full autonomic cycle of the SELFNET framework
Summary
Traffic generated by video applications has increasingly dominated 3G/4G mobile networks, placing great strain on network capacity. It will contribute to performance improvements through faster deployment times and reduced service discontinuity It will place the user at the heart of network management decision making by adopting a QoE based approach to service level monitoring and delivery. It will reduce operational costs and help to meet carbon reduction targets by operating in an end-to-end energy efficient manner. Further highly innovative and challenging QoE estimation techniques for video services such as M2M and automated surveillance are planned to be addressed In these types of service, quality is measured by the utility of the information contained in a video when performing a task such as object recognition, target tracking, or decision making tasks.
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