Monitoring video quality is essential for any operator — but it doesn’t have to be expensive. Hardware costs often make up a significant part of monitoring budgets, especially as the number of channels and streams grows.
The key lies not in monitoring less, but in monitoring smarter, so one can significantly reduce costs while keeping full control over service quality.
Let’s look at a practical approach to cost optimization using real monitoring scenarios.
Step 1. Monitor What Really Matters
In many real-world setups, teams are responsible for both encoding and content delivery. Trying to apply the same level of deep analysis everywhere quickly leads to unnecessary resource consumption.
A more efficient approach is to split responsibilities:
At the encoding headend
Focus on QoE (Quality of Experience) monitoring. This helps detect encoding-related issues early.During content delivery
Shift the focus to QoS (Quality of Service) metrics. Here, the priority is stable signal transport.
This separation allows you to reduce the overall monitoring load without losing visibility into critical problem areas.
Step 2. Cut Hardware Costs with Lite QoE Mode
If you are using the Elecard Boro monitoring system, one of the most effective ways to reduce hardware requirements is enabling Lite QoE mode.
What is Lite QoE?
Lite QoE performs semantic analysis of the elementary stream (ES) without full video decoding. Instead of processing every frame in detail, the system analyzes stream structure and key parameters. Measurement accuracy is approximate, but sufficient to assess video decodability and detect key playback issues. This mode offers medium resource consumption, significantly lowering CPU load.
Why it matters
- Medium resource consumption
- Significantly lower CPU load
- Allows monitoring of more channels on the same hardware
For many monitoring tasks, Lite QoE provides exactly the level of insight needed — without the cost of full decoding.
Practical Example: Frame Rate Monitoring with Fewer Resources
Let’s look at a real-life situation.
A video stream declares a 29.97 fps frame rate in its headers. However, during playback, the video stutters and doesn’t look smooth. Since the corresponding checks are disabled, the monitoring system doesn’t raise an alarm.
How can different monitoring modes handle this?
- Full decode mode
The system decodes each frame to detect deeper-level irregularities. While this provides more detailed diagnostics, it’s much more resource-intensive, increasing CPU load and overall system cost.
- Semantic frame header analysis
The system simply counts frame headers over time to estimate the actual frame rate. This method is lightweight, requires minimal CPU power, and is sufficient to confirm that playback irregularities exist.
It perfectly fits the goal of reducing hardware usage while maintaining essential control over video quality.
Thus, clear and flexible monitoring configuration options can significantly optimize operational costs.
The Result: Smarter Monitoring, Lower Costs
By choosing the right monitoring depth for each stage of the workflow and using flexible modes like Lite QoE, operators can:
- Lower CPU load
- Maintain essential video quality control
- Detect real issues before they impact viewers
Clear and flexible monitoring configuration isn’t just a technical advantage — it’s a direct way to optimize operational costs while keeping services reliable.
Want to go further?
In a dedicated document, we’ve collected four more practical use cases showing how to solve real-world broadcasting challenges.
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