5G Traffic Classification for Extended Reality Applications

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Solution Overview

Problem

Current 5G NR networks face challenges in optimizing performance for Extended Reality (XR) applications due to variable file sizes and interactive nature of XR traffic, which are not adequately addressed by existing FTP traffic models, requiring adaptive classification of XR-specific traffic flows with RAN-level characteristics for resource allocation, link adaptation, and radio resource management.

Innovation Solution

The implementation of a system that receives XR traffic characteristic information from a radio access network (RAN) node, maps it to user, bearer, or traffic flow, and modifies RAN parameters based on this information, including resource grant allocation, scheduler metrics, and radio resource management parameters, to optimize per-user and system performance and QoS parameters, with feedback loops to XR application servers for further adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing FTP traffic models are used for 5G NR networks, then general traffic management is maintained, but XR-specific performance optimization is insufficient due to variable file sizes and interactive nature

Engineering Contradiction:

Improvetraffic classification adaptabilityVSAvoidQoS guarantee

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent segments traffic into different categories including XR-specific traffic flows, video traffic, and general data traffic. This segmentation allows the system to apply specialized handling mechanisms to XR traffic while maintaining standard protocols for other traffic types, thereby achieving both adaptability for XR applications and reliability for general QoS requirements.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements dynamic traffic classification and QoS parameter adjustment based on real-time network conditions and XR application requirements. The system continuously monitors traffic characteristics and adapts resource allocation, latency parameters, and data rates dynamically, enabling the network to respond to variable XR traffic patterns while maintaining reliable service delivery.

Inventive Principle:

Principle #15Dynamics

2Productivity

If adaptive classification of XR-specific traffic flows is implemented, then performance optimization for XR applications is achieved, but system complexity increases

Engineering Contradiction:

Improvenetwork performanceVSAvoidtraffic management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent establishes preliminary QoS configurations and traffic classification rules specifically designed for XR applications before traffic arrives. By pre-defining handling mechanisms for XR traffic characteristics (variable file sizes, interactive nature), the system reduces the complexity of real-time decision-making while maintaining high network performance for XR services.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces intermediary components such as traffic classifiers, QoS managers, and mapping functions that mediate between raw XR traffic and network resource allocation. These intermediaries simplify the overall system architecture by centralizing complex classification and management logic in dedicated modules, thereby improving network performance without proportionally increasing overall system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If RAN parameters are modified based on XR traffic characteristics, then per-user and system performance is optimized, but control complexity increases

Engineering Contradiction:

Improveuser experience qualityVSAvoidparameter control complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies local quality optimization by tailoring RAN parameter modifications to specific user requirements and traffic characteristics. Instead of uniform parameter adjustments across all users, the system selectively modifies parameters such as resource grant allocation, scheduler metrics, and radio resource management parameters based on individual XR application needs, thereby improving user experience quality while managing control complexity through targeted rather than blanket adjustments.

Inventive Principle:

Principle #3Local quality

4Measurement precision

If feedback loops to XR application servers are implemented, then traffic classification accuracy is improved, but signaling overhead increases

Engineering Contradiction:

Improvetraffic classification precisionVSAvoidsignaling overhead

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent implements partial feedback mechanisms where only critical traffic characteristics and performance metrics are exchanged between the network and XR application servers. By selectively transmitting essential information rather than complete traffic data, the system achieves sufficient classification precision for XR applications while minimizing signaling overhead to acceptable levels.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240049039A15g air interface-based traffic classification for extended reality (XR)

Publication Date: 2024.02.08 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, systems and methods for enabling traffic classification for extended reality (XR) applications over 5G NR networks. XR-specific traffic flow are adaptively classified with RAN-level characteristics including resource allocation, link adaptation, and radio resource management (RRM) metrics and configurations to optimize per-user and system performance and QoS parameters. Other embodiments are disclosed.