Article 3 — Inferring QoE Without a Call: Predicting Videoconferencing Experience from Ambient Telemetry

Most QoE tools wait for a problem to occur. They analyse what happened during a call — packet loss, jitter, resolution drops — and report after the fact. Useful, but reactive.

QoE AI Insights takes a different approach. It uses ambient handset telemetry — the signals your phone is already generating — to infer how well a videoconference would perform if it were to happen right now.

This article explores how that inference works, why it’s reliable, and how it enables proactive experience assurance.

Ambient Telemetry: The Signals That Never Sleep

Even when no call is active, the handset is constantly reporting conditions that shape videoconferencing performance:

• Wi-Fi RSSI and stability

• 4G/5G signal quality (RSRP, RSRQ, SINR)

• Jitter and packet loss at the device edge

• Mobility events and handovers

• Background data contention

• Power-saving modes affecting throughput

These signals are present all the time — not just during a call. They define the environment in which a video session would succeed or struggle.

QoE AI Insights listens to these signals and builds a probabilistic model of expected performance.

What “Likely QoE” Really Means

QoE AI Insights doesn’t simulate a call. It doesn’t guess. It correlates ambient telemetry with known patterns of videoconferencing behaviour.

For example:

• Low Wi-Fi RSSI → likely video resolution drops

• High jitter → likely audio instability

• Frequent handovers → likely freeze‑and‑recover cycles

• Packet loss → likely degradation in both audio and video clarity

This creates a real-time, evidence-based prediction of how a call would behave under current conditions.

It’s not speculative. It’s grounded in how real apps (Teams, Zoom, Webex, Meet) respond to stress.

Validating Inference with Test Videoconferencing Calls

To complement passive inference, QoE AI Insights can also initiate controlled test calls — lightweight synthetic video/audio sessions that measure real-time performance.

These tests:

• Run under current network conditions

• Capture jitter, packet loss, throughput, and stability

• Validate the inferred QoE predictions

• Provide a benchmark for expected meeting quality

This dual approach — inference plus validation — gives operators and enterprises confidence in the insights.

From Prediction to Action

QoE AI Insights doesn’t just say “QoE might be poor.” It provides actionable indicators:

• “High likelihood of audio degradation due to jitter”

• “Moderate risk of video freezes from mobility events”

• “Low expected QoE due to weak Wi-Fi signal”

These insights can be:

• Used by support teams to triage issues

• Integrated into dashboards for proactive monitoring

• Triggered as alerts for experience degradation

• Fed into automation systems for dynamic remediation

Why This Matters

For Enterprises

• Understand remote worker meeting readiness

• Identify users at risk before they complain

• Improve hybrid work reliability

For Service Providers

• Offer proactive QoE assurance

• Reduce support tickets

• Build experience-centric SLAs

For End Users

• Fewer surprises when joining calls

• More confidence in their connectivity

• Better overall meeting experience

Looking Ahead: Article 4 — Measuring QoE with Synthetic Test Calls

In the next article, we’ll explore how test calls work, what they measure, and how they complement ambient telemetry to provide a full picture of videoconferencing readiness.