Video Network Bandwidth Control for Camera Transmission Reliability
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Solution Overview
Problem
Network bandwidth issues in surveillance camera systems lead to delays and video loss due to unidirectional video traffic and spiky frame transmission, which are often addressed reactively and require costly upgrades.
Innovation Solution
Active monitoring and adjustment of camera outbound traffic bandwidth using iterative adjustments to stabilize video transmission with minimal delay and no losses, employing network configuration modules and rules to identify and rectify discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network bandwidth capacity is increased to handle video traffic from multiple cameras, then video transmission reliability is improved, but network equipment cost and complexity increase
Solution Approach 1:
The system performs preliminary actions by proactively monitoring network parameters and adjusting camera bandwidth limits before video transmission failures occur. The monitoring component continuously tracks network conditions, and when potential bandwidth issues are detected, the system automatically adjusts camera settings to prevent packet loss and transmission delays, rather than waiting for problems to manifest.
Solution Approach 2:
The system implements feedback mechanisms by comparing transmitted video data with received video data, monitoring network parameters, and using this information to automatically adjust camera bandwidth limits. The discrepancy detection component identifies when video data is not received within expected timeframes, and this feedback loop enables continuous optimization of network performance without manual intervention.
2Difficulty of detecting and measuring
If manual network topology analysis is performed to identify bottlenecks, then network performance issues are diagnosed, but time and operational resources are consumed
Solution Approach 1:
The system enables self-service by automatically monitoring network parameters, detecting bandwidth issues, and adjusting camera settings without requiring manual network analysis. The monitoring component continuously tracks network conditions, and the discrepancy detection component automatically identifies problems, eliminating the need for manual topology analysis and reducing operational resource consumption.
Solution Approach 2:
The system performs preliminary monitoring and detection actions continuously in the background, so when network bottlenecks occur, they are already identified and being addressed. This proactive approach eliminates the need for reactive manual analysis and reduces the time required to diagnose and resolve network performance issues.
3Speed
If camera bandwidth limits are increased to reduce transmission delays, then video transmission speed is improved, but network packet loss increases
Solution Approach 1:
The system applies dynamics by continuously adjusting camera bandwidth limits based on real-time network conditions rather than using static settings. The monitoring component tracks network parameter changes, and the system dynamically modifies camera transmission rates to optimize both speed and reliability, allowing the bandwidth limit to adapt as network conditions change.
Solution Approach 2:
The system implements parameter changes by modifying camera bandwidth limits based on monitored network conditions. When network capacity is sufficient, bandwidth limits are increased to improve transmission speed; when congestion is detected, limits are adjusted to prevent packet loss. This dynamic parameter adjustment optimizes the trade-off between transmission speed and delivery reliability.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium for identifying discrepancies in video networks, comprising receiving a network topology of a video network comprising a plurality of cameras and a network video recorder (NVR), wherein the network topology comprises identifiers of cameras and network hardware in the video network; monitoring network parameters of the video network; detecting a discrepancy in the network parameters, wherein the discrepancy indicates that data from at least one camera in the plurality of cameras is not received by the NVR within a threshold period of time; identifying, using a plurality of rules and the network topology, a location of the discrepancy in the video network; and generating a graphical user interface that indicates the location of the discrepancy on the network topology.