Latency Management for XR Devices via Network Scheduler
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Solution Overview
Problem
Existing solutions for managing latency in extended reality (XR) devices using wireless connections often detect or predict latency and jitter too late or inaccurately, leading to frustrating and potentially motion-sickness-inducing performance degradation.
Innovation Solution
A network-implemented paradigm that proactively determines when latency or jitter is likely to occur by monitoring radio resource allocation, allowing XR devices to take preemptive mitigating actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If device-based solutions detect or predict latency and jitter, then mitigation actions can be taken, but the mitigation is either too late (detection-based) or imprecise (prediction-based)
Solution Approach 1:
The network scheduler performs preliminary action by proactively scheduling XR traffic with prioritized resource allocation before latency issues occur. The system predicts potential latency conditions and preemptively allocates radio resources to prevent degradation, rather than reacting after detection or using imprecise predictions.
Solution Approach 2:
The system implements feedback through continuous monitoring of latency and jitter conditions, where network measurements are fed back to adjust scheduling decisions. This closed-loop feedback enables real-time adaptation of resource allocation based on actual network conditions, improving mitigation precision.
2Productivity
If radio resources are allocated to XR devices, then XR experience is maintained, but latency and jitter increase when resources are insufficient
Solution Approach 1:
The system changes scheduling parameters dynamically based on network conditions and XR device requirements. The scheduler adjusts radio resource allocation parameters, prioritization levels, and timing parameters to maintain stable connections while adapting to varying resource availability and latency conditions.
Solution Approach 2:
The resource allocation system is dynamic, continuously adapting to changing network conditions, device requirements, and traffic patterns. The scheduler makes real-time decisions about resource distribution based on current system state, enabling flexible response to latency and jitter conditions.
Data Source
AI summary
Embodiments of the present disclosure are directed to systems and methods for predicting high latency conditions for a data connection between an extended reality device and a wireless telecommunication system. A schedule associated with downlink and/or uplink grants at a base station is queried to determine if it comprises a sufficient number of grants in an upcoming time period to adequately serve the extended reality device. If it does not, the extended reality device can implement mitigating actions to reduce data consumption in the downlink or data transmission in the uplink to minimize undesirable effects to user experience and device performance.