Understanding Videoconferencing QoE Through Handset Telemetry and Targeted Test Calls

Videoconferencing quality is shaped by far more than what happens during a live meeting. The conditions that determine whether a Teams, Zoom, or Webex call will feel smooth and reliable are already visible in the handset’s everyday telemetry — long before anyone joins a call.

QoE AI Insights focuses on this broader context:

using handset‑level network and radio telemetry, combined with optional test videoconferencing calls, to understand the conditions that drive real‑world meeting performance.

Handset Telemetry: A Window Into Likely Videoconferencing Quality

Even outside an active call, the device continuously reveals the environment it’s operating in. These signals are directly relevant to videoconferencing QoE:

• Wi‑Fi signal strength and stability

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

• Cell handovers and mobility patterns

• Local network congestion

• Packet loss and jitter at the device edge

• Background data usage

• Power‑saving modes that may constrain throughput

These factors don’t suddenly appear when a meeting starts — they shape the experience from the moment the user connects.

By analysing this telemetry, QoE AI Insights can infer the likely performance of a videoconference under current conditions.

From Telemetry to Experience: How QoE Is Inferred

QoE AI Insights maps handset conditions to well‑understood patterns of videoconferencing behaviour. For example:

• Weak Wi‑Fi → likely video resolution drops or freezes

• High jitter → likely audio instability

• Radio fluctuations → likely short stalls or reconnection events

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

This creates a practical, evidence‑based view of how a call would typically behave in similar conditions.

It’s not speculative — it’s grounded in the real environment the device is experiencing.

Test Videoconferencing Calls: Measuring Performance Directly

In addition to passive telemetry, QoE AI Insights can also initiate controlled test videoconferencing sessions. These tests:

• Use lightweight, synthetic video/audio streams

• Measure real‑time performance under current network conditions

• Capture jitter, packet loss, throughput, and stability

• Provide a direct benchmark of expected meeting quality

This gives organisations a way to validate experience proactively — without waiting for a user to join a real meeting or report an issue.

Test calls complement handset telemetry by offering a ground‑truth measurement of how the network behaves under videoconferencing load.

What QoE AI Insights Actually Provides

1. A unified view of handset‑level network conditions

Normalised across devices, networks, and environments.

2. Clear interpretation of experience‑impacting factors

Focused on the conditions that matter most for videoconferencing.

3. Experience‑centric indicators

Such as:

• “Audio quality risk due to jitter”

• “Potential video instability from Wi‑Fi fluctuations”

• “Connectivity variability likely to affect meeting smoothness”

4. Optional test‑call validation

A direct measurement of expected videoconferencing performance.

5. Actionable guidance

Grounded in real device and network conditions.

Why This Matters

For Enterprises

• Understand the environment users are working in

• Identify QoE risks before they become support issues

• Validate meeting readiness with test calls

For Service Providers

• Deliver proactive experience assurance

• Reduce escalations tied to device‑edge or access‑network issues

• Offer measurable, experience‑centric SLAs

For End Users

• More predictable meeting performance

• Fewer surprises when joining calls

• Greater confidence in their connectivity

A More Complete Approach to Videoconferencing QoE

QoE isn’t defined only by what happens during a call. It’s shaped by the device’s network environment and radio conditions — and can be validated through targeted test calls when needed.

By combining handset telemetry with controlled videoconferencing tests, QoE AI Insights provides a practical, proactive understanding of the conditions that drive real meeting performance.

It’s a grounded, operational way to understand and improve videoconferencing QoE across diverse devices, networks, and environments.