…and fixes issues before users notice.
When your catalogue spans seven languages, three packaging formats (MP4, HLS, DASH/CMAF) and a growing mix of AVC– and HEVC-based bitrate ladders while rolling out UHD and serving a heterogenous variety of device types, operating systems and browsers, the odds of a silent playback glitch go up every day.
That’s a small glimpse at the reality at ARTE, the Franco-German public broadcaster best known for early 4K-UHD content, a highly popular Smart-TV app, and even an Apple Vision Pro catalogue.
For years ARTE’s engineering and operations teams have prided themselves on delivering excellent viewing experience, quietly keeping error rates well below industry averages. But scaling that reliability gets harder as the platform footprint keeps expanding.
Earlier in 2025, ARTE partnered with EINBLIQ.IO to answer the questions:
- How do we guarantee flawless playback for every viewer?
- How do we stay in control as devices, codecs and languages multiply?
- How do we give engineers back time for product work?
Below is what we deployed together, what we learned and why it matters to any streamer juggling similar growth pains.
Crucially, the observability stays embedded in ARTE’s workflow—no new tools to check, no parallel ticketing.
1. Complexity in one screenshot
Before talking solutions, it helps to visualise the surface area the team has to cover. The table below captures the moving parts as of Q3 2025.
| Axis | Q3/2025 snapshot for ARTE |
|---|---|
| 4 app platforms served | Web browsers Smart TV: HbbTV, Tizen, webOS, Roku, Vidaa, TiVo… Android: Mobile, Android TV, Google TV, Fire OS Apple: Mobile, Apple TV, Apple Vision Pro |
| 2 codecs | AVC (H.264) HEVC (H.265) |
| 3 packaging formats | HLS DASH/CMAF Progressive MP4 |
| up to 7 languages (UI, audio, subtitles) | French, German, English, Spanish, Polish, Italian, Romanian |
Multiply those axes and you get hundreds of relevant permutations. This goes well beyond manual test coverage.
Legacy devices still matter; so does every new device that claims to play HEVC but quietly falls back to software decode, which can
cause stutter at higher resolutions (and unnecessary energy use).
2. What ARTE and EINBLIQ.IO built together
Rather than chasing every permutation by hand, the joint effort focused on instrumentation and automation. Most components below went live in less than two weeks.
| Layer | What it does | Why ARTE wanted it |
|---|---|---|
| Player integration | Sends granular playback metrics at least every 60s | One-line integration; works with legacy HbbTV OIPF as well |
| CMCD log analytics | Standards-based insights via CDN logs | 360° observability and a validation pilot for CMCD |
| ML/AI clustering | Auto-groups needles-in-the-haystack into coherent incidents | Removes threshold tuning burden; cuts alert noise |
| Energy lens | Per-session Wh model | Helps balance quality and environmental footprint |
| Slack + Jira bridge | Pre-filled tickets with all relevant context | Speeds triage; stays in ARTE’s stack (Jira + Slack; SSO respected) |
3. An incident that would normally stay invisible
The very first week in production delivered an illustrative example:
| Step 1 | Specific cluster of HbbTV devices plays ABR content at SD instead of HD. |
| Step 2 | Viewers stay silent; average session length drops 19% for that cluster. |
| Step 3 | Monitoring surfaces the cluster and suggests an update (app + packaging settings). |
| Step 4 | ARTE receives a ticket with specific error description including a pre-filtered dashboard link. |
| Step 5 | ARTE repackages and adapts app on staging; tests validate the fix. |
| Step 6 | After the change, the average viewing duration returns to normal. |
No static threshold would have caught that: set them too high and you miss the edge case; too low and you drown in noise.
4. Why ML/AI clustering beats threshold alerts
Here’s where the data-driven clustering approach can outperform classic threshold alerting.
| Topic | EINBLIQ.IO clustering with ARTE | Threshold-based approach |
|---|---|---|
| Setup effort | Learns from real data | Complex and easy to miss relevant criteria |
| Rare-issue detection | Also finds low-volume but coherent patterns | Typically blind to edge-cases |
| Alert fatigue | Only relevant incidents (ranked by estimated impact) | Less predictable |
| Root-cause context | Relevant facets already attached | Manual digging |
| Maintenance | Feedback on alerts continuously improves models | Constant tweaking |
5. Shortening the path from first symptom to validated fix inside ARTE’s toolchain
The common goal for ARTE and EINBLIQ.IO is straightforward: Spend less time on detective work so ARTE’s engineers can invest more energy in new features and forward-looking projects.
To make progress visible, we track five operational KPIs. The table below shows how each metric is expected to develop. We plan to publish quantitative results in the near future.
| KPI | Before: Viewer complaint is the only trigger | Intermediate: If a viewer complaint is received | Target: Pattern detected before any complaint |
|---|---|---|---|
|
Viewer-Complaint Rate (VCR) Share of total incidents first reported by viewers |
100% of what gets noticed; many issues stay invisible | Falls sharply — only residual long-tail | 0% — pattern detection flags issue first |
|
Time-to-Detect (TTD) Issue appears → first signal/alert |
Hours/days after first impact, if at all | Lower with direct metric correlation | Immediately after first affected sessions |
|
Time-to-Root-Cause (TTRC) Detection → root cause isolated and documented |
High: Manual triage, log digging, device reproduction | Lower: ticket already includes isolated cluster + dashboard link | ≈ 0: root cause shipped with detection |
|
Time-to-Resolve (TTR) Ticket created → fix validated in telemetry |
High: Copy/paste into ticket, manual validation | Pre-filled Jira ticket, fix loop starts sooner | Pre-filled Jira ticket, fix loop starts earliest |
|
Engineering Effort (EE) Engineer hours spent across detect/diagnose/fix |
High: Reproduce, cluster, file ticket | Lower: Most triage automated | Lowest: Effort goes into the actual fix |
A real-world illustration: After an update, for certain Android devices of a major vendor, video playback stalled as soon as viewers swiped to the next clip on the ‘ARTE Shorts’ page.:
Detection – The pattern detection immediately noticed a spike in stall events for a list of devices when playing back “Shorts” portrait videos; no viewer had yet complained.
Root-cause context – The issue was confirmed with BrowserStack and the auto-generated Jira ticket already listed the affected device models plus the relevant evidence and traces, so triage time was effectively zero for ARTE.
Remediation – Engineers modified the player configuration in the app and validated the fix straight from the linked dashboard.
Total turnaround was about three hours, saving roughly five staff-hours compared with the old flow. The issue was fixed before the first user complaints were received.
(More TTD / TTRC / TTR analyses for additional scenarios will appear in a follow-up post.)
6. What’s next
| Quality control for streaming app updates and new features |
| Data-driven UHD roll-out |
| Field-data comparison Player SDK vs. CMCD (CMCD v2 soon) |
| Tuned bitrate ladders based on bandwidth, reliability and energy |
| Agentic workflows: System auto-investigates, isolates root cause, and either applies guarded fixes or posts mitigations to Jira. |
7. Get the full story
We’re finalising a detailed white paper with architecture diagrams, data-flow visuals and before/after graphs.
👉 Want a sneak peek or a live demo? Ping us here or Meet us at IBC 2025