Wi-Fi Access Point QoE Scheduling for Mixed Traffic Latency
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Solution Overview
Problem
Conventional Wi-Fi networks struggle to dynamically prioritize and schedule traffic flows based on Quality of Experience (QoE) scores, leading to inefficiencies in managing latency-sensitive applications and competing traffic flows, resulting in poor user experience due to jitter and delay, especially in mixed traffic scenarios with Overlapping Basic Service Set interference.
Innovation Solution
Implementing mechanisms to prioritize traffic flows based on QoE scores computed for each flow, assigning them to queues with weighted priority, using MU grouping and transmission modes like OFDMA, and optimizing resource allocation to enhance QoE visibility and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Wi-Fi networks manage traffic flows without dynamic prioritization, then device complexity is reduced, but Quality of Experience deteriorates due to jitter and delay in latency-sensitive applications
Solution Approach 1:
The patent implements dynamic QoE scoring that continuously evaluates traffic flows and adjusts prioritization in real-time based on current network conditions and application requirements, transforming static traffic management into a dynamic adaptive system that resolves the contradiction between reliability and complexity
Solution Approach 2:
The system establishes a feedback loop where QoE scores are computed for each traffic flow, prioritization decisions are made based on these scores, and the results are monitored to continuously refine prioritization, creating a self-optimizing system that improves Quality of Experience while managing complexity through automated control
2Loss of time
If traffic flows are prioritized based on QoE scores, then latency-sensitive applications improve, but competing traffic flows may experience starvation
Solution Approach 1:
The patent changes the parameter of traffic prioritization from static QoS labels to dynamic QoE scores that reflect actual user experience, allowing the system to identify and prioritize latency-sensitive applications based on real-time conditions while maintaining fairness through score-based differentiation
Solution Approach 2:
The system applies partial prioritization by using QoE scores to differentiate treatment among traffic flows rather than applying uniform prioritization, giving enhanced service to latency-sensitive applications while ensuring competing flows receive adequate bandwidth through score-based proportional allocation
3Adaptability or versatility
If multiple transmission protocols like OFDMA and MU-MIMO are used, then flexibility in supporting varying operations is improved, but device complexity and difficulty of managing different modes increases
Solution Approach 1:
The patent creates a universal QoE-based prioritization framework that works across multiple transmission protocols (OFDMA, MU-MIMO, traditional Wi-Fi), allowing a single prioritization mechanism to serve multiple functions and protocols, thereby reducing the complexity of managing different transmission modes while maintaining adaptability
4Reliability
If QoE scoring and prioritization mechanisms are implemented, then user experience is enhanced, but computational overhead and processing requirements increase
Solution Approach 1:
The system implements partial QoE evaluation by focusing computational resources on scoring and prioritizing only latency-sensitive traffic flows rather than all traffic, reducing overall computational overhead while still enhancing user experience for critical applications through selective processing
Data Source
AI summary
Systems and methods are provided for traffic flow scheduling optimization through prioritizing traffic flows according to a Quality of Experience (QoE) score. Examples receive data packets of a plurality of traffic flows by an access point of a network, map the data packets of the plurality of traffic flows to a plurality of queues based on traffic identifiers of each data packet, and determine a QoE score for each traffic flow of the plurality of traffic flows. Examples can then prioritize the data packets based on the QoE scores determined for the plurality of traffic flows and schedule the data packets for transmission or reception based on the prioritization.