Intelligent Network Video Player Dynamic Rate Limiting
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Solution Overview
Problem
Existing solutions like Stream Saver® fail to dynamically adjust video and elastic traffic rates based on network conditions, leading to suboptimal user experience and resource utilization, as they apply static settings regardless of network quality.
Innovation Solution
A system that analyzes key performance indicators (KPIs) to dynamically adjust video and elastic traffic rates using machine learning and reinforcement learning algorithms, ensuring optimal throughput and quality of service by reallocating resources based on real-time network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static rate limiting is applied to all video streams regardless of network conditions, then network capacity is conserved and capital expenditures are reduced, but video user experience deteriorates when network quality is good and elastic user experience deteriorates when network quality is poor
Solution Approach 1:
The patent implements dynamic rate limiting that automatically adjusts video stream bandwidth allocation based on real-time network conditions. The system monitors network quality metrics and dynamically modifies rate limits for different video streams, transitioning from static to adaptive control. This resolves the contradiction by making the rate limiting mechanism flexible enough to preserve network capacity during congestion while maximizing user experience when bandwidth is available.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor network conditions and user experience metrics, then use this information to adjust rate limiting parameters. By implementing closed-loop control where network performance data feeds back into rate limit adjustments, the system can respond to changing conditions and optimize both network resource utilization and user experience dynamically.
2Ease of manufacture
If network rate limiting is increased to reduce capital expenditures, then video traffic is constrained, but video user experience and quality of service deteriorate
Solution Approach 1:
The patent changes the parameter of rate limiting from fixed to variable by introducing dynamic adjustment mechanisms. The system modifies rate limit parameters based on network conditions, traffic patterns, and service level requirements. This allows the network to enforce stricter limits when resources are constrained (reducing CAPEX) while relaxing limits when capacity is available (maintaining QoS), thus resolving the contradiction between cost reduction and service quality.
3Productivity
If dynamic rate adjustment based on network conditions is implemented, then user experience and throughput are improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the rate limiting function into modular components that can be independently managed and processed. By dividing the complex dynamic rate adjustment task into separate modules (monitoring, analysis, decision-making, enforcement), the system reduces overall complexity while maintaining dynamic functionality. Each segment handles a specific aspect of rate adjustment, making the system more manageable and scalable.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, selecting a first rate limit for a conveyance of first video traffic in a communication system based on an analysis of a first plurality of key performance indicator (KPI) values for the communication system, conveying the first video traffic in the communication system in accordance with the first rate limit, and conveying first elastic traffic in the communication system based on the first rate limit. Other embodiments are disclosed.