Distributed MPEG Transport Stream Analysis System
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Solution Overview
Problem
Existing cable systems lack real-time monitoring and analysis of transport streams (TSs) across a geographic region, leading to inadequate insight into TS quality and performance between generation and reception points.
Innovation Solution
A real-time distributed moving picture experts group (MPEG) TS analysis system that utilizes multiple parts of the network to concurrently monitor TSs across a geographic region. This system selects profiles for each TS and collection point, including acceptable transmission values and rules, and adapts these profiles and rules in real-time to maintain service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time distributed monitoring of multiple TSs across a geographic region is implemented, then TS quality and performance insight is improved, but system complexity increases
Solution Approach 1:
The system divides the geographic region into multiple collection points, each independently monitoring TSs. Each collection point operates as a separate monitoring unit that collects and analyzes TS data locally, then reports findings to the central system. This segmentation allows comprehensive regional coverage while keeping individual monitoring units relatively simple.
Solution Approach 2:
The monitoring system is designed to handle multiple TSs simultaneously across all collection points using a unified architecture. The system can concurrently process, analyze, and compare data from numerous transport streams through standardized procedures, enabling multi-functional operation without proportionally increasing complexity at each node.
2Reliability
If profiles and rules are adapted in real-time for each TS and collection point combination, then service quality maintenance is improved, but processing requirements increase
Solution Approach 1:
Acceptable transmission value ranges and monitoring rules are pre-established for each TS and collection point combination before actual monitoring begins. These profiles contain predetermined thresholds and criteria that guide real-time analysis, eliminating the need for complex on-the-fly decision-making and reducing processing demands during operation.
Solution Approach 2:
The system dynamically adapts profiles and rules based on changing service conditions and network states. When services associated with TSs evolve or network conditions change, the system automatically adjusts monitoring parameters and acceptable value ranges to maintain appropriate service quality thresholds without requiring complete reconfiguration.
3Productivity
If concurrent monitoring of multiple TSs at multiple collection points is performed, then network-wide TS analysis capability is improved, but data processing load increases
Solution Approach 1:
The system extracts and focuses on specific critical parameters and key quality indicators from the full TS data stream at each collection point. Rather than processing all raw data, the system identifies and monitors only the most relevant transmission characteristics and quality metrics, significantly reducing data volume while maintaining monitoring effectiveness.
Solution Approach 2:
The system implements monitoring at strategic collection points throughout the geographic region rather than attempting to monitor every possible location continuously. This selective approach provides sufficient network-wide coverage and insight into TS quality while keeping the overall data processing load manageable through targeted sampling and analysis.
Data Source
AI summary
Embodiments include a system and method for a real-time distributed Transport Stream (TS) analysis that utilizes many parts of an available network to concurrently monitor TSs across a geographic region. Embodiments include the selection of a profile for each combination of a TS and a collection point where the profile includes one or more sets of acceptable transmission values or ranges associated with the combination. The profile may include for example, program service information tables, service information, TS transmission parameters, and/or general TS quality index analysis rules. Embodiments include real-time concurrent monitoring and analysis of multiple TSs from collection points distributed throughout the geographic region. As services associated with the TSs at different collection points evolve, the respective profiles and rules adapt accordingly to maintain the quality of service and performance associated with respective TS and collection point combinations.