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5G Radio Network Optimization for VR/AR/XR
Lead Guest Editor:
Huichun Liu
QUALCOMM Wireless communication Technologies (China) Limited, Beijing, China
Introduction
5G has introduced many techniques to support very large variability of communication needs (from enhanced mobile broadband, to mission-critical control, to massive Internet of Things). Yet, VR/AR/XR are still challenging to 5G because VR/AR/XR require strict end-to-end low latency, however, ultra-low latency as low as 1 millisecond in 5G air interface cannot guarantee the end-2-end delay sensitive service requirement, and it will make the user feel dizzy in the VR/AR/XR experience. Solutions and techniques to reduce the end-2-end latency is strongly needed, e.g. mobile edge content caching/delivery at RAN (mobile CDN), transport network optimization. For the mobile edge content delivery, security issue and mobility issue need to be further studies.
Moreover, 5G QoS provisioning is semi-static and is constant along lifetime of the session. However, the data burst of the most promising service such as VR/AR/XR is very high and traffic throughput demand may variant very dramatically. The dynamic can be caused by user interaction, e.g. video refresh, VR helmet or handle interactions. More real-time service prioritization in RAN is required to adapt more quickly to the application layer traffic to avoid a stalling of video, e.g application service type and traffic model can be aware by RAN with some cross-layer optimization or machine learning method. On the other hand, if app layer is aware of radio signal situation and transport network status, app layer can also make some adaption to the fast-changing radio network, e.g by throughput predication via machine learning.
In general, advanced techniques and solutions for the burst traffic with high data-rate and stringent low latency requirement for both downlink and uplink are needed.

Aims and Scope:

  1. VR/AR/XR optimization
  2. E2E Low latency
  3. Mobile CDN
  4. Service aware RAN
  5. Cross-layer optimization
  6. Machine learning
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