How QoE AI Insights Helps Operators Finally See Real Video Performance

Moving from network‑centric KPIs to experience‑centric operations

After years of relying on throughput charts, drive tests, and generic KPIs, operators are realising something fundamental, Video experience is now the primary lens through which customers judge network quality.

And yet, most operators still lack a clear, continuous, and objective view of how well video streaming actually performs across their networks.

QoE AI Insights changes that — by giving operators a direct, device‑level understanding of real video behaviour across their market.

The Visibility Gap: Why Operators Struggle With Video QoE

Traditional tools don’t capture what customers feel:

• Throughput doesn’t tell you if YouTube is stuck at 480p

• Latency doesn’t reveal TikTok stalling

• Drive tests miss indoor usage and mid‑range devices

• Customer complaints arrive long after the damage is done

Operators need a way to see:

• How long video takes to start

• Whether sessions reach HD, Full HD, or 4K

• How often rebuffering occurs

• Where performance drops geographically

• How different device categories behave

This is the layer of truth that has been missing.

The Solution: AI‑Driven Video QoE From Real Operator Devices

QoE AI Insights uses advanced AI models to infer video performance directly from customer smartphones running real apps under real conditions.

This gives operators a continuous, representative, and operationally meaningful view of:

• Video Start Time (VST)

The strongest predictor of customer satisfaction.

• Resolution Adaptation

How often sessions reach HD, Full HD, or 4K — and where they don’t.

• Buffering Events

The moments that instantly break customer trust.

• Geo‑Granular Performance

Town‑level, cluster‑level, and cell‑level visibility.

• Device‑Level Behaviour

How mid‑range Android devices perform vs. premium devices.

Impact of Network Changes

Immediate feedback on optimisation, new spectrum, or vendor tuning.

Because the data comes from the operator’s own devices, the insights are clean, controlled, and aligned with the operator’s real customer base.

Why AI Is the Breakthrough

Video adaptation is shaped by a complex mix of:

• App logic

• CDN selection

• Device capability

• Radio conditions

• Network load

• Local geography

Historically, this complexity made video QoE difficult to measure at scale.

AI changes that.

QoE AI Insights uses machine learning to:

• Detect adaptation patterns

• Infer QoE from encrypted traffic

• Identify root causes behind poor experience

• Predict where issues will emerge

This shifts operators from reactive troubleshooting to proactive optimisation.

The Impact: Experience‑Centric Operations That Deliver

Operators using QoE AI Insights typically see:

• Fewer video‑related complaints

• Higher NPS and brand perception

• Smarter, more targeted capex

• Faster validation of network changes

• Clearer operational priorities

It’s a direct path to networks built around what customers actually feel.

Why This Matters Now

5G adoption, UHD streaming, and short‑form video have raised expectations dramatically. Legacy KPIs can’t keep up.

Operators who understand real video QoE will lead.

Those who don’t will fall behind — quietly at first, then suddenly.

The Future: Networks Built Around Experience

QoE AI Insights enables operators to:

• Build networks around real user experience

• Prioritise investments based on customer impact

• Validate changes with confidence

• Deliver video performance customers can feel

Experience‑centric operations aren’t a trend — they’re the new baseline.

QoE AI Insights is the bridge.