QoE-Driven Network Resource Allocation for XR Split Rendering
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Solution Overview
Problem
Current communication systems, particularly those supporting extended reality (XR) applications, face challenges in ensuring optimal quality of experience (QoE) due to constraints in bandwidth, latency, and computing power, which affect the performance of XR services that require high resource demands and stringent latency requirements.
Innovation Solution
A method and apparatus that determine target performance indicator values associated with a desired QoE on a user device and communicate these values to a network entity, using a quality of experience model to request necessary resources, such as throughput and delay, to support XR applications, enabling split rendering and real-time processing of complex video scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are allocated to support high data rate and low latency for XR applications, then quality of experience is improved, but network bandwidth and resource availability are constrained
Solution Approach 1:
The system performs preliminary determination of target performance indicator values associated with desired quality of experience before actual XR application execution. The user device or network entity pre-calculates required throughput and delay parameters, and the network entity pre-allocates resources based on these predetermined targets, ensuring QoE requirements are met without over-provisioning network bandwidth
Solution Approach 2:
The system dynamically adjusts network resource allocation based on actual application performance and user device requirements. The user device monitors actual performance indicators against target values and sends feedback to the network entity, which then dynamically modifies resource allocation (throughput, delay parameters) to maintain optimal quality of experience while adapting to changing network conditions and application demands
2Speed
If edge cloud processing is used to reduce latency for split rendering, then real-time processing capability is improved, but network complexity increases
Solution Approach 1:
The rendering process is segmented into multiple components: local rendering on the user device and cloud-based rendering on the edge cloud. The system divides the video scene processing into portions that can be handled locally and portions that require cloud processing, with the network entity coordinating resource allocation for each segment. This segmentation enables real-time processing by distributing computational load while managing network complexity through structured communication protocols
Data Source
AI summary
A method comprises: determining on a user device, one or more target performance indicator values associated with a target quality of experience for an application running on the user device; and causing a request to be sent to a network entity with information about the one or more target performance indicator values.