Why Video Conferencing Apps Need Audio-Video Testing?

Microsoft Teams, Google Meet, and Zoom together serve hundreds of millions of users worldwide. At the same time, workers say they’re overloaded with meetings, and audio/video problems are frequent enough to damage productivity and employee experience. These facts make audio and video quality testing an essential part of any conferencing product release and operations strategy.

This blog post breaks down what audio-video quality assurance (QA) means for modern conferencing apps, the technical metrics you must measure, some testing scenarios you shouldn’t skip, tools, as well as common pitfalls to avoid, in order to reduce defects and keep meeting platforms reliable.

Why audio-video quality matters

High-quality audio and video features in conferencing apps define the user experience. In remote and hybrid environments, every meeting is an opportunity to communicate clearly, make decisions quickly, and maintain team cohesion. When audio drops or video freezes, the momentum of a conversation breaks. People repeat themselves, confusion grows, and productivity suffers. At scale, these seemingly small interruptions could create real business costs.

AV quality directly influences user retention, support ticket volume, and the credibility of the platform. With millions of users relying on conferencing tools every day, even minor quality regressions can affect thousands of meetings within minutes. That makes consistent audio and video performance not only a technical priority, but a strategic one.

Here’s what recent data shows:

Large user bases mean any AV regression impacts millions of meetings and has real business consequences for both customers and vendors.
Surveyed workers report meeting overload; poor meeting quality amplifies wasted time and frustration.
Industry measurements and vendor studies show poor AV can affect employee satisfaction, retention, and the return-to-office experience — AV problems are more than a nuisance; they’re a business risk.

Audio quality metrics

Before diving into the specifics, it’s important to understand why audio metrics deserve special attention. In most meetings, audio—not video—is the primary channel of communication. Users can tolerate a slightly blurry webcam, but even a few seconds of distorted, delayed, or dropped audio can derail an entire conversation. That’s why audio testing focuses heavily on clarity, stability, and low latency.

Modern conferencing platforms rely on adaptive codecs, noise suppression, echo cancellation, and network conditioning to deliver consistent results across unpredictable environments. To evaluate whether these systems are performing as expected, QA teams need reliable, repeatable metrics that reflect real-world perception, not just raw technical outputs.

Here are the core audio metrics that should guide your testing strategy:

Latency (one-way and round-trip) — voice needs low end-to-end latency to feel natural.
Jitter and jitter buffer behavior — unrestrained jitter leads to gaps and rebuffering.
Packet loss and loss concealment effectiveness — higher packet loss should be masked by the codec and FEC/PLC logic.
Echo return loss enhancement (ERLE) and double-talk handling — ensures echo cancelers don’t suppress the speaker when both parties talk.
Signal-to-noise ratio (SNR) and residual background noise level — measure noise suppression quality.
Voice activity detection and automatic gain control behavior — important for consistent levels across participants.

Video quality metrics

Video quality plays a major role in how users perceive professionalism, engagement, and trust during a meeting. While audio is essential for communication, video adds important visual cues—facial expressions, gestures, and reactions—that enhance clarity and collaboration. Poor video quality can slow down presentations and make it harder for participants to stay focused.

Unlike audio, video demands much more bandwidth and processing power, which makes it more vulnerable to network fluctuations, device limitations, and codec inefficiencies. This is why video testing must capture both the visual experience and the underlying performance of the adaptive video pipeline.

To understand how your platform behaves under real-world conditions, here are the key video metrics you should be measuring:

Mean Opinion Score (MOS) or an subjective MOS estimate — a single-number proxy for perceived video quality.
Frame rate (fps) and frame drops — smooth motion depends on a stable frame rate.
Resolution and effective visual clarity — ensuring the image is sharp enough for face-to-face interactions or slide presentations.
Bitrate, bitrate adaptation, and bitrate oscillation patterns — critical for handling fluctuating network conditions.
Keyframe interval and recovery time — important for how quickly the video returns to clarity after packet loss or motion.
Latency and A/V sync — misalignment between audio and video disrupts perception.
Freeze duration, frequency, and decoder stalls — common user-visible failures that harm meeting flow.
End-to-end rendering time (capture → encode → transport → decode → render) — reflects true video responsiveness.

Test scenarios you must cover

Understanding metrics is only half the battle. The next step is designing test scenarios that reflect real-world usage. Users join meetings from different networks, devices, and locations, often under unpredictable conditions. A conferencing app that performs flawlessly in the lab but fails in actual use will quickly frustrate users and erode trust.

Effective testing covers three main dimensions: network conditions, devices and platforms, and real-world human behavior. By combining these, QA teams can simulate the complex environments in which meetings actually happen, uncover hidden defects, and ensure a reliable experience.

Network tests

Baseline: good Wi-Fi or wired connection with no impairment.
Variable bandwidth: throttle uplink/downlink across typical ranges (e.g., 128 kbps to 5 Mbps).
High packet loss or burst loss patterns, variable jitter, and asymmetric links.
Mobile handoffs (Wi-Fi ↔ LTE/5G) and high-latency conditions, such as satellite networks.
NAT/firewall traversal and TURN relay path behavior.

Device and platform tests

Low-end mobile devices versus high-end desktops; CPU and thermal throttling scenarios.
Browser versus native app behaviors (e.g., WebRTC differences across Chrome, Edge, Safari).
USB headsets, Bluetooth headsets, integrated microphones/speakers, and AEC interactions in conference rooms.

Codec and features

Test with all supported audio and video codecs (Opus, AAC, VP8, VP9, H.264, AV1).
Validate echo cancellation, noise suppression, automatic gain control, stereo/mono modes, simulcast, and SVC behavior.
Verify adaptive bitrate algorithms under rapid network changes.

Edge cases and human behavior

Multiple simultaneous speakers, overlapping speech, and raised-hands scenarios.
Screen sharing with active webcam; presenter switching and layout changes.
Background noise sources (keyboard, music, traffic) and virtual background impact on CPU.
Accessibility features: captions, sign-language windows, and low-vision support.

Integrating test automation

Automated testing is a powerful ally in ensuring consistent audio and video quality, especially for repetitive and high-volume testing. While human perception is ultimately necessary for subjective evaluation, automating routine tests frees QA teams to focus on more complex scenarios, exploratory testing, and user experience validation.

Test automation is particularly effective for regression testing, load testing, and continuous monitoring of AV performance. By integrating automated tests into your development pipeline, you can catch quality regressions early, reduce release risks, and maintain a high standard across frequent updates.

Key areas to automate

Continuous integration tests: Encode and decode reference audio/video files and compute objective MOS scores to detect regressions.
Load and stress tests: Simulate hundreds or thousands of concurrent calls to evaluate server performance, resource usage, and QoS degradation.
Canary deployments: Run synthetic calls in select regions or environments to monitor AV quality before a full rollout.
Regression monitoring: Automatically track metrics like frame drops, packet loss, latency, and MOS across versions.
Alerts and thresholds: Configure automatic alerts when AV quality dips below acceptable levels, enabling rapid response before users notice.

However, remember that, even with automation, periodic manual testing is essential to add the human evaluation factor. Consider testers joining live calls, simulating real meeting behaviors, and judging perceived audio and video quality provide invaluable insights that metrics alone can’t capture.

Phones on tripods set up for audio-video testing

Common pitfalls and how to avoid them

Even experienced QA teams can fall into traps that compromise audio-video quality. Understanding common pitfalls helps teams anticipate challenges, design better tests, and ensure a reliable user experience across platforms.

Relying only on synthetic network tests: Lab conditions can’t capture every real-world scenario. Combine with real-user monitoring and canary deployments to catch issues early.
Ignoring CPU/thermal impacts of advanced features: Virtual backgrounds, AI noise suppression, or video filters can overburden devices, leading to stutters or crashes. Include long-duration tests on a range of devices.
Trusting objective metrics alone: MOS scores and latency measurements don’t always reflect perceived quality. Pair quantitative metrics with human evaluation.
Skipping negative testing: Edge cases like intermittent NAT failures, aggressive firewalls, or sudden bandwidth drops often reveal hidden weaknesses. Include scenarios that intentionally stress the system.

Recommended tool categories

Selecting the right tools is critical for effective AV testing. The complexity of modern conferencing apps—spanning devices, networks, codecs, and advanced features—requires a combination of network emulation, protocol analysis, device testing, and monitoring tools.

Using the right tools helps QA teams automate tests, capture meaningful metrics, and quickly diagnose issues before they reach users. Take a look at the list below.

Network emulation: Tools like tc/netem or commercial WAN emulators to simulate packet loss, jitter, latency, and bandwidth fluctuations.
Protocol analysis and tracing: Wireshark or RTCP/XR parsing for deep inspection of audio/video streams. WebRTC getStats collectors provide real-time telemetry.
Synthetic load testing: Call generators, Selenium, or Puppeteer for automated browser flows to test scalability and performance under load.
Device testing: Real-device farms or in-house labs to validate behavior across mobile, desktop, and hardware configurations.
Monitoring and dashboards: Time-series databases with Grafana or equivalent dashboards to track MOS, packet loss, latency, CPU, and memory metrics.
Subjective testing tools: Moderated user sessions, MOS panels, and UX labs to assess perceived quality that metrics alone can’t capture.

Final thoughts

Audio and video quality are at the heart of user trust in conferencing platforms. Clear, stable audio and smooth, reliable video are no longer optional—they define whether meetings are productive, engaging, and frustration-free. Delivering this experience requires a combination of rigorous testing, real-user monitoring, automation, and human evaluation.

Start by benchmarking your current audio and video performance, identifying gaps, and expanding coverage to real-world conditions. Implement deterministic tests, synthetic probes, and structured QA processes to detect regressions before they reach your users. Advanced features, accessibility, and edge-case scenarios should be tested alongside core AV pipelines to maintain quality across all platforms.

If your team wants to elevate your conferencing app’s AV performance, our experts can run a tailored audio-video quality audit. We’ll benchmark your app under real-world conditions, produce a practical test matrix, set up telemetry dashboards, and design canary tests to catch regressions before they affect users.