solution Use Case

Supporting end-user OTT streaming clients with DataMiner

Last updated on the 3rd of March 2025
RabbitMQ OTT

This use case demonstrates how DataMiner can support end-user OTT streaming clients, including Apple TV, Android TV, mobile devices, Chromecast, and more, by leveraging data from RabbitMQ and OpenSearch. By exploiting a combination of DataMiner EPM (Experience and Performance Management) and offloaded events to OpenSearch, second-line operators can efficiently diagnose and resolve issues affecting end-user streaming experiences through a low-code app.

Data collection and processing

  • Error & event ingestion:
    • Streaming clients generate diagnostic events, errors, and activity logs.
    • These logs are received via RabbitMQ and offloaded into OpenSearch for indexing and retrieval.
  • DataMiner integration:
    • DataMiner EPM collects and trends performance metrics related to streaming quality, network conditions, and device health.
    • OpenSearch provides enriched event information, enabling enhanced analysis.

Analysis and issue detection

  • Trend analysis & anomaly detection:
    • DataMiner correlates trended performance data with OpenSearch event logs.
    • Anomalies, such as buffering, playback failures, or DRM issues, are identified.
  • Root cause analysis:
    • DataMiner classifies issues (e.g. network limitations, CDN failures, client-side problems).
    • Historical trends help differentiate transient issues from persistent problems.

Operator support & issue resolution

  • Low-code app for second-line operators:
    • The app provides a real-time dashboard summarizing per-client issues.
    • The app displays detected problems, root cause insights, and suggested resolutions.
  • Automated recommendations:
    • Based on analytics, DataMiner suggests troubleshooting actions.
    • Operators receive step-by-step guidance on resolving detected issues.
  • Proactive monitoring & alerts:
    • DataMiner generates alerts for recurring or critical issues.
    • Proactive measures can be recommended.

Outcome & benefits

  • Improved troubleshooting efficiency: Operators resolve issues faster with data-driven insights.
  • Enhanced user experience: Faster identification and mitigation of streaming problems.
  • Operational optimization: Data-driven decisions help reduce downtime and improve service reliability.

USE CASE DETAILS

In this example, you can see how a low-code app displays bandwidth limitations for an FWA customer, indicates that the average chunk download bitrate is too low, that the Wi-Fi connection is OK, and that there are many stalled events.
In this pane, you can find a collection of aggregated error events related to the TV channel, helping you identify recurring issues. This provides additional insights alongside the other available statuses in DataMiner, including relationships in the Service layer.

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