Adaptive Traffic Management for Cellular Networks
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Solution Overview
Problem
Cellular wireless network operators face challenges in managing explosive data traffic growth, with existing traffic management techniques like lossy compression potentially degrading user experience due to the slow pace of network upgrades and high upgrade costs not matching revenue growth.
Innovation Solution
An adaptive traffic management system that uses a traffic processor to determine historical content data availability, a user experience monitor to calculate user experience metrics, and a predictor to generate expected quality of experience scores for optimizing current content data, applying traffic management rules based on network probes, subscriber profiles, and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are applied to optimize web and video traffic, then network congestion is reduced and download time is improved, but user experience quality is degraded
Solution Approach 1:
The patent implements dynamic selection between lossless and lossy compression techniques based on real-time network conditions. The system monitors network congestion levels and user device characteristics, then adaptively chooses the appropriate compression method. When network conditions are good, lossless compression is used to preserve quality. When congestion is detected, lossy compression is applied to reduce bandwidth consumption, thus resolving the contradiction between network throughput and content quality.
Solution Approach 2:
The system changes compression parameters dynamically based on network conditions. It adjusts the compression level, format, and technique applied to web and video content according to real-time metrics such as network speed, congestion level, and device capabilities. This parameter adaptation allows the system to optimize for either quality or throughput depending on current conditions, resolving the fixed trade-off between compression efficiency and content quality.
2Reliability
If network upgrades are implemented to meet growing data traffic demand, then quality of experience is improved, but costs increase significantly
Solution Approach 1:
The patent creates virtual network capacity through intelligent traffic management and optimization techniques. Instead of physically upgrading network infrastructure, the system uses software-based solutions including traffic compression, caching, and routing optimization to effectively increase network capacity. This virtualization approach delivers improved quality of experience without the high costs associated with physical network expansion.
Solution Approach 2:
The system optimizes network performance by dynamically adjusting operational parameters such as traffic routing paths, bandwidth allocation, and protocol settings. These parameter changes enable the existing network infrastructure to handle increased data traffic more efficiently, achieving the效果 of network upgrades without actual hardware expansion.
3Productivity
If traffic management techniques are applied to control data traffic growth, then network efficiency is improved, but user experience may be degraded
Solution Approach 1:
The patent implements dynamic traffic management that adapts to real-time network conditions and user needs. The system continuously monitors network state and user device characteristics, then dynamically adjusts traffic management techniques such as compression level, caching strategy, and protocol optimization. This dynamic approach ensures that traffic management actions are taken only when necessary and are tailored to preserve user experience while improving network efficiency.
Solution Approach 2:
The system applies different traffic management techniques to different types of traffic, users, and network conditions rather than using a uniform approach. It identifies specific traffic flows and applies appropriate optimization methods locally, such as applying compression only to suitable content types or using aggressive caching for frequently accessed resources while leaving other traffic unaffected. This localized approach improves network efficiency without broadly degrading user experience.
Data Source
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AI summary
A method including determining whether historical content data is available for a time period; acquiring historical content data associated with a web content source if the historical content data is available; determining one or more user experience metrics for a specific terminal based on the acquired historical content data; and acquiring an expected quality of experience score based at least in part on the determined one or more user experience metrics, wherein the expected quality of experience score can be used for providing one or more traffic management optimization rules for current content data to the specific terminal.