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Optimizing media streaming for an entertainment company

Why do playback failures spike when the CDN still has headroom?

About the company

A player in the entertainment industry offering on-demand and live media streaming to hundreds of thousands of subscribers worldwide — where every playback start depends on backend services responding within tight latency budgets.
Optimizing media streaming for an entertainment company

Industry

Media & Entertainment

Key challenge

Playback failures and degraded responsiveness during live events and peak evening hours; no end-to-end visibility into backend service behaviour under concurrent load

Stack under test

HTTPS REST APIs (catalog, authentication, session management), Redis (session state and entitlement cache), Apache Kafka (analytics event pipeline)

QALIPSIS deployment

Cluster mode with geographically distributed factories

Challenges

Why do playback failures spike during live events?

  • Post-incident analysis pointed to backend services, not the CDN.
  • Each playback start triggered a chain of API calls dependent on cache and messaging.
  • Existing tools could stress individual endpoints but not the integrated pipeline.

Solution: how QALIPSIS was used

How to simulate realistic viewer sessions?

  • HTTP steps replicated the full playback sequence: login, entitlement, content lookup, session start.
  • Stages execution profile reproduced a live premiere: steep surge, sustained peak, tail-off.

How to verify session state in the cache layer?

  • Redis plugin checked that each new viewer session was correctly stored in the cache.
  • Join operators matched each API-created session against its cached record.
  • Overload exposed: cache writes fell behind under peak session-creation rate.

How to validate analytics event capture?

  • Kafka plugin consumed analytics events alongside HTTP load injection.
  • Join operators matched each viewer action against its expected analytics event.
  • Event loss exposed: analytics pipeline dropped events when overwhelmed under peak load.

How to detect regional performance differences?

  • Load generated from multiple geographic zones revealed latency differences by region.
  • Root cause identified: session service deployed in a single region penalised remote viewers.
  • Fix: session state redesigned for a geographically distributed store.
  • Login now routes each viewer to the nearest regional instance, removing cross-region round-trips.

Results

fewer playback failures
more concurrent users supported
more complete analytics capture
improved regional consistency

Conclusion

Challenge

Playback failures and buffering during live events caused by backend degradation invisible to endpoint-level testing.

Solution

QALIPSIS combined HTTPS session simulation, Redis cache verification, and Kafka analytics validation in a geographically distributed campaign.

Gains

70% fewer playback failures, 30% more concurrent viewers, 100% analytics capture, and cross-region latency resolved through regional replication.

More use cases to explore

Ensure smooth streaming experiences with QALIPSIS.
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