1. Qi Alfred Chen University of Michigan
QoE Doctor: Diagnosing Mobile App QoE with Automated UI Control and Cross-layer Analysis
Qi Alfred Chen
University of Michigan

2. Challenge
How can we effectively and systematically study the QoE of popular mobile apps?
How can we analyze mobile app QoE which is affected by factors at many layers of the system and the network.

3. Design QoE Metrics User-perceived latency Mobile Data consumption
Energy consumption

4. Design QOE-AWARE UI CONTROLLER
Application control
Control the app UI through the InstrumentationTestCase API. (re-signed APK file)
Follow a see-interact-wait paradigm.
Require some familiarity with Android UI View classes

5. Design QOE-AWARE UI CONTROLLER
App-specific Control Design and User-perceived Latency Collection
Facebook
Upload post
Pull-to-update
YouTube
Watch video(initial loading time & rebuffering ratio)
Web Browsing
Load web page(page loading time)

6. Design QOE-AWARE UI CONTROLLER
Data Collection
Application Layer Data Collection
Collected by the wait component
Transport/Network Layer Data Collection
Collected by tcpdump
RRC/RLC Layer Data Collection
Collected by QxDM

7. Design MULTI-LAYER QOE ANALYZER
Application Layer Analyzer
User-perceived latency calibration
Transport/Network Layer Analyzer
Calculate mobile data consumption

8. Design MULTI-LAYER QOE ANALYZER
RRC/RLC Layer Analyzer
Obtain RRC state change information from QxDM logs
Obtain the power level from Monsoon Power Monitor

9. Design MULTI-LAYER QOE ANALYZER
Cross Application, Transport/Network Layers
Identify root causes of QoE problems in the application layer(e.g. device latency or network latency)
Cross Transport/Network, RRC/RLC Layers
Which RRC state transition cause user-perceived latency
Understand how network packets are transmitted in the lower layer

10. Evaluation Facebook: Post Uploading Time Breakdown Analysis
Finding 1: The network delay is not always on the critical path

11. Evaluation YouTube: Advertisement Impact on Initial Loading Time
Finding 9. Advertisements reduce the initial loading time of the actual video, but double the total initial loading time

12. Evaluation Other findings
Finding 2: 3G RLC transmission delay contributes more than expected in the end-to-end photo posting time
Finding 3. Facebook’s non-time-sensitive background traffic adds non-negligible overhead to users’ daily mobile data and energy consumption
Finding 4. Changing one Facebook configuration can reduce mobile data and energy consumption caused by non-timesensitive background traffic by 20%
……(10 findings in total)

13. Evaluation
Tool Accuracy and Overhead

14. Conclusion
Built a tool, QoE Doctor, which can automatically and repeatly collect QoE data.
Analyzed QoE metrics across multi-layer, and diagnosed QoE problems and identify the root causes.
Measured QoE metrics in popular Android apps, and quantify how various important factors impact these QoE metrics.