Root Cause Analysis Service for Video Streaming
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Solution Overview
Problem
Current content delivery systems, such as video streaming, face challenges in predicting and addressing issues like latency, interruption, and low throughput due to limitations in existing standards for measuring and managing video stream stability, leading to sub-optimal user experiences.
Innovation Solution
A content delivery infrastructure that incorporates a root cause analysis (RCA) service using machine learning to identify and correct issues by processing content delivery metrics, leveraging pre-trained language transformer models and explainable AI to provide real-time solutions for degrading content delivery streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measurement standards (ETR 101) are used for transport streams, then basic metrics and errors can be reported, but predictive indication of video stream stability is not provided
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical transport stream data and support ticket information before actual video stream failures occur. This enables predictive indication of stability issues before they manifest, allowing proactive rather than reactive measurements.
Solution Approach 2:
The patent introduces machine learning models as an intermediary layer between traditional measurement standards and video stream stability assessment. These models process raw metrics from ETR 101 standards along with support ticket data to generate predictive stability indications, bridging the gap between basic error reporting and advanced prediction.
2Ease of operation
If manual analysis of support tickets is performed, then detailed issue investigation is possible, but real-time automated solutions cannot be provided
Solution Approach 1:
The system enables self-service by automatically analyzing support tickets and generating resolutions without requiring manual human intervention. The machine learning models process ticket data, identify patterns, and produce actionable solutions autonomously, freeing operators from repetitive manual analysis while maintaining high-quality troubleshooting.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated machine learning systems. Instead of human operators manually reviewing support tickets and diagnosing issues, ML models automatically process the data, identify root causes, and generate solutions, substituting human cognitive processes with computational algorithms.
3Reliability
If comprehensive metrics collection is implemented, then better diagnostic information is available, but system complexity increases
Solution Approach 1:
The system achieves universality by using a single machine learning framework that processes multiple types of input data (transport stream metrics, support ticket information, system logs) through a unified model. This multi-functional approach consolidates what would otherwise require separate specialized systems for each data type, reducing overall complexity while maintaining comprehensive diagnostic capabilities.
Data Source
AI summary
A method and system corrects a content delivery infrastructure. The method of the system includes receiving a request to resolve reported issues for the content delivery infrastructure, collecting content delivery metrics for the content delivery infrastructure, executes a language transformer model on the request and the content delivery metrics to generate a set of possible resolutions with confidence ratings, and implementing an automated solution based on a resolution from the set of possible resolutions, in response to the resolution having a confidence rating above a threshold.