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Product Management Root Cause Analysis Question: Investigating Google Meet video quality degradation

Asked at Google

15 mins

Why did Google Meet video quality drop to 480p for 50% of users?

Problem Solving Technical Analysis Data Interpretation Technology Telecommunications Enterprise Software
User Experience Google Root Cause Analysis Technical Troubleshooting Video Conferencing

Introduction

The sudden drop in Google Meet video quality to 480p for 50% of users is a critical issue that demands immediate attention and a thorough root cause analysis. This problem not only affects user experience but also threatens Google Meet's competitive position in the video conferencing market. I'll approach this analysis systematically, focusing on identifying the underlying causes, validating hypotheses, and developing both short-term fixes and long-term solutions.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the scale of the issue, I'm thinking this might be a system-wide problem. Can you confirm if this is affecting users globally or in specific regions?

Why it matters: Understanding the geographical scope helps narrow down potential infrastructure or network-related issues. Expected answer: Global issue Impact on approach: If global, we'd focus on core systems; if regional, we'd investigate local network conditions.

  • Considering the specific quality drop, I'm wondering about recent codec or bandwidth management changes. Have there been any recent updates to video compression algorithms or bandwidth allocation policies?

Why it matters: Recent changes could directly impact video quality. Expected answer: Yes, a recent update was deployed Impact on approach: If yes, we'd prioritize investigating that update; if no, we'd look at other potential causes.

  • Given the 50% user impact, I'm curious about user segmentation. Can you provide any information on common characteristics among affected users (e.g., device types, browsers, or connection types)?

Why it matters: Identifying patterns in affected users can point to specific technical or environmental factors. Expected answer: No clear pattern identified yet Impact on approach: If patterns exist, we'd focus on those segments; if not, we'd need to cast a wider net in our investigation.

  • Thinking about the timing, I'm wondering if this coincides with any significant traffic spikes or changes in usage patterns. Has there been any unusual increase in concurrent users or meeting durations recently?

Why it matters: Sudden changes in usage could strain system resources and affect quality. Expected answer: Some increase in usage, but not unprecedented Impact on approach: If usage has spiked, we'd investigate scaling issues; if not, we'd look more at system-level problems.

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