Dynamic Scheduling Prioritization for Live Streaming QoS
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Solution Overview
Problem
The existing 5GS and EPS QoS frameworks fail to provide useful differentiation of data traffic up to the expected quality bitrate, as they focus on fulfilling Guaranteed Flow Bit Rate (GFBR)/Guaranteed Bit Rate (GBR) and use Maximum Flow Bit Rate (MFBR)/Maximum Bit Rate (MBR) only to limit bit rates, leading to suboptimal quality and cost trade-offs, with increased risk of bearer rejection and quality degradation.
Innovation Solution
Introduce a graceful scheduling prioritization behavior for traffic bitrates above GFBR/GBR but below MFBR/MBR, allowing for dynamic assignment of scheduling priorities based on expected quality levels, and introduce new signaling parameters to inform RAN scheduler nodes about traffic priority behavior between these thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the QoS framework focuses on fulfilling Guaranteed Flow Bit Rate (GFBR)/Guaranteed Bit Rate (GBR) and uses Maximum Flow Bit Rate (MFBR)/Maximum Bit Rate (MBR) only to limit bit rates, then the guaranteed service level is maintained, but the quality and cost trade-offs become suboptimal with increased risk of bearer rejection
Solution Approach 1:
The patent introduces dynamic scheduling prioritization that adjusts resource allocation in real-time based on current network conditions and traffic characteristics. The scheduler dynamically modifies scheduling priorities for different QoS flows within the GBR-MBR range, enabling adaptive quality optimization without compromising the guaranteed service level. This dynamic approach allows the system to respond to changing conditions and optimize quality-cost trade-offs continuously.
Solution Approach 2:
The patent changes the scheduling priority parameter dynamically based on the traffic flow's bitrate position within the GBR-MBR range. By adjusting the scheduling priority parameter according to current service quality and expected quality level, the system enables flexible quality optimization while maintaining the guaranteed bit rate floor. This parameter change mechanism resolves the contradiction by making the QoS system adaptable within the guaranteed service framework.
2Adaptability or versatility
If dynamic scheduling prioritization is introduced to optimize quality within GBR-MBR range, then quality and cost trade-offs improve, but the system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the scheduler continuously monitors current service quality for each QoS flow and adjusts scheduling priorities accordingly. The system measures actual performance, compares it with expected quality levels, and dynamically modifies scheduling decisions based on this feedback. This feedback-driven approach enables quality optimization without requiring complex manual configuration or centralized control, as the system self-adjusts based on observed conditions.
Solution Approach 2:
The scheduling system performs self-optimization by automatically adjusting priorities based on inherent performance measurements and pre-configured quality expectations. The network nodes autonomously make scheduling decisions without external intervention, using locally available information about current service quality and expected quality levels. This self-service mechanism reduces operational complexity while maintaining adaptive quality optimization.
3Productivity
If scheduling priority is dynamically adjusted based on current service quality and expected quality level, then sustained expected quality bitrate is achieved, but the computational overhead increases
Solution Approach 1:
The patent applies partial action by adjusting scheduling priorities only for QoS flows whose current bitrate falls within the GBR-MBR range and whose quality needs optimization. Flows already meeting or exceeding expected quality levels maintain their current priorities, avoiding unnecessary computational adjustments. This selective approach achieves sustained quality bitrate for flows that need it while minimizing computational overhead by not continuously re-evaluating all flows.
Data Source
AI summary
According to certain embodiments, a method by a first network node operating as a Radio Access Network (RAN) node for dynamic scheduling prioritization for live uplink streaming includes receiving at least one priority level. Based on the at least one priority level, an expected quality level is determined. The expected quality level is defined as a Quality of Service (QoS) of a QoS flow being scheduled with a service quality above a minimum quality level and below a maximum quality level. A current service quality for a plurality of QoS flows is determined. Based on the combined current service quality and the expected quality level, a scheduling priority for assigning resources to a plurality of QoS flows is determined.