Network Trace Categorization for Adaptive Bandwidth Estimation
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Solution Overview
Problem
Adaptive bitrate algorithms in content streaming systems face challenges in accurately estimating current bandwidth due to fluctuations in network conditions, leading to inconsistent viewing experiences.
Innovation Solution
A system decomposes network traces into high and low frequency sub-traces, focusing on the low frequency sub-trace for quality of service analysis, using change point detection to determine sub-segments and extract features for categorization, which adjusts parameters in content delivery processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If short-term network measurements are used for bandwidth estimation, then the algorithm can respond quickly to network changes, but the estimation accuracy is insufficient
Solution Approach 1:
The patent segments the network trace into multiple frequency components using Fourier transform, separating high-frequency transient variations from low-frequency stable patterns. This segmentation allows the system to analyze different frequency components separately, improving estimation accuracy by focusing on the stable low-frequency components while maintaining responsiveness through selective use of high-frequency information.
Solution Approach 2:
The patent extracts the low-frequency sub-trace from the complete network trace by filtering out high-frequency components. This extraction isolates the stable, predictable network behavior patterns that are most useful for accurate bandwidth estimation, while discarding the noisy high-frequency fluctuations that reduce accuracy.
2Loss of information
If complete network traces are analyzed in detail, then comprehensive insights are obtained, but computing resources are consumed
Solution Approach 1:
The patent extracts only the essential low-frequency components from the complete network trace, discarding the computationally expensive high-frequency components that contribute less to accurate bandwidth estimation. This extraction maintains the essential information needed for QoS analysis while significantly reducing the computational burden.
Solution Approach 2:
The patent uses a simplified analysis approach for high-frequency components, processing them with less computational effort compared to the detailed analysis applied to low-frequency components. This differential processing strategy allocates computing resources efficiently, focusing detailed analysis where it matters most.
3Reliability
If all frequency components of network traces are used for analysis, then complete network behavior is captured, but the analysis complexity increases
Solution Approach 1:
The patent segments the network trace analysis into distinct frequency-based components, allowing the system to handle different components with appropriate complexity levels. The low-frequency components receive detailed analysis while high-frequency components are processed more simply, reducing overall analysis complexity while maintaining reliability.
Solution Approach 2:
The patent extracts and focuses analysis on the low-frequency sub-trace, which contains the most relevant information for bandwidth estimation and QoS prediction. By extracting this essential component, the system reduces analysis complexity while maintaining the reliability needed for accurate network behavior capture.
Data Source
AI summary
In some embodiments, a method receives a network trace from a playback session of a delivery of content being managed by a content management system. The network trace is decomposed into a first sub-trace and a second sub-trace based on a threshold. The threshold is used to divide the network trace into the first sub-trace and the second sub-trace. A value is extracted for a feature for the network trace based on the first sub-trace. The method classifies the network trace into a category based on the value and performs an action based on the category for the content management system.