1. Measuring Interaction QoE in Internet Videoconferencing Prasad Calyam (Presenter) Ohio Supercomputer Center, The Ohio State University Mark Haffner, Prof. Eylem Ekici Prof. Chang-Gun Lee The Ohio State University Seoul National University MMNS, November 1st 2007

2. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

3. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

4. Voice and Video over IP (VVoIP) Overview
Large-scale deployments of VVoIP are on the rise
Video streaming (one-way voice and video)
MySpace, Google Video, YouTube, IPTV, …
Video conferencing (two-way voice and video)
Polycom, MSN Messenger, WebEx, Acrobat Connect, …
Challenges for large-scale VVoIP deployment
Real-time or online monitoring of end-user Quality of Experience (QoE)
Traditional network Quality of Service (QoS) monitoring not adequate
Network QoS metrics: bandwidth, delay, jitter, loss
Need objective techniques for automated network-wide monitoring
Cannot rely on end-users to provide subjective rankings – expensive and time consuming

5. Network QoS and End-user QoE
End-user QoE is mainly dependent on the combined impact of network factors
Device factors such as voice/video codecs, peak video bit rate (a.k.a. dialing speed) also matter
Our study maps the network QoS to end-user QoE for a given set of commonly used device factors
H.263 video codec, G.711 voice codec, 256/384/768 Kbps dialing speeds
Network QoS
End-user QoE

6. Voice and Video Packet Streams
Total packet size (tps) – sum of payload (ps), IP/UDP/RTP header (40 bytes), and Ethernet header (14 bytes)
Dialing speed is ; = 64 Kbps fixed for G.711 voice codec
Voice has fixed packet sizes (tpsvoice ≤ 534 bytes)
Video packet sizes are dependent on alev in the content

7. Video alev Low alev High alev ‘Listening’ versus ‘Talking’
Slow body movements and constant background; E.g. Claire video sequence
High alev
Rapid body movements and/or quick scene changes; E.g. Foreman video sequence
‘Listening’ versus ‘Talking’
Talking video alev(i.e., High) consumes more bandwidth than Listening video alev (i.e., Low)
Foreman
Claire

8. End-user QoE Types Streaming QoE Interaction QoE
End-user QoE affected just by voice and video impairments
Video frame freezing
Voice drop-outs
Lack of lip sync between voice and video
Interaction QoE
End-user QoE also affected by additional interaction effort in a conversation
“Can you repeat what you just said?”
“This line is noisy, lets hang-up and reconnect…”
QoE is measured using “Mean Opinion Score” (MOS) rankings

9. Network Fault Events
“Best-effort service” of Internet causes network fault events that impact application performance
Cross traffic congestion, routing instabilities, physical link failures, DDoS attacks
Our definition of network fault events is based on the “Good”, “Acceptable” and “Poor” (GAP) performance levels for QoS metrics causing GAP QoE
Type-I: Performance of any network factor changes from Good grade to Acceptable grade over a 5 second duration
Type-II: Performance of any network factor changes from Good grade to Poor grade over a 10 second duration

10. Related Work Characteristics of network fault events well understood
Bursts, spikes, complex patterns – lasting few seconds to a few minutes (Markopoulou et. al., Ciavattone et. al.)
Measuring Streaming QoE impact due to network fault events has been well studied
ITU-T E-Model is a success story for VoIP QoE estimation
Designed for CBR voice traffic and handles only voice related impairments
ITU-T J.144 developed for VVoIP QoE measurement
“PSNR-based MOS” – Requires original and reconstructed video frames for frame-by-frame comparisons
Offline method - PSNR calculation is a time consuming and computationally intensive process
Online VVoIP QoE measurement proposals
PSQA (G. Rubino, et. al.), rPSNR (S. Tao, et. al.)
Measuring Interaction QoE impact due to network fault events has NOT received due attention
Need for schemes to measure interaction difficulties in voice and video conferences presented by A. Rix, et. al.

11. Multi-Activity Packet Trains Methodology
Problem Summary
Given:
Voice/video codecs used in a videoconference
Dialing speed of the videoconference
Network fault event types to monitor
Develop:
An objective technique that can measure Interactive VVoIP QoE
Real-time measurement without involving actual end-users, video sequences and VVoIP appliances
An active measurement tool that can: (a) emulate VVoIP traffic on a network path, and (b) use the objective technique to produce Interaction QoE measurements
Multi-Activity Packet Trains Methodology
Vperf Tool

12. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

13. Proposed Solution Methodology
“Multi-Activity Packet Trains” (MAPTs) measure Interaction QoE in an automated manner
They mimic participant interaction patterns and video activity levels as affected by network fault events
Given a session-agenda, excessive talking than normal due to unwanted participant interaction patterns impacts Interaction QoE
“Unwanted Agenda-bandwidth” measurement and compare with baseline (consumption during normal conditions)
Higher values indicate poor interaction QoE and caution about potential increase in Internet traffic congestion levels
Measurements serve as an input for ISPs to improve network performance using suitable traffic engineering techniques

14. Proposed Solution Methodology (2)
‘repeat’
‘disconnect’
‘reconnect’
‘reorient’
Type-I and Type-II fault detection

15. Participant Interaction Patterns
Assumption: Question (Request) and Answer (Response) items in a session agenda
Side-A listening when Side-B talking, and vice versa
Normal – PIP1

16. Participant Interaction Patterns (2)
Participant Interaction Patterns (PIPs) using MAPTs for a “Type-I” network fault event
Repeat – PIP2

17. Participant Interaction Patterns (3)
Participant Interaction Patterns (PIPs) using MAPTs for a “Type-II” network fault event
Disconnect/Reconnect/Reorient – PIP3

18. Participant Interaction Patterns (4)
Our goal is to measure the Unwanted Agenda-bandwidth and Unwanted Agenda-time measurements after MAPTs emulation of the Q & A session agenda

19. Traffic Model for MAPTs Emulation
Traffic Model for probing packet trains obtained from trace-analysis
Combine popularly used low and high alev video sequences and model them at 256/384/768 Kbps dialing speeds for H.263 video codec
Low – Grandma, Kelly, Claire, Mother/Daughter, Salesman
High – Foreman, Car Phone, Tempete, Mobile, Park Run
Modeling
Video Encoding Rates (bsnd) time series
Packet Size (tps) distribution
Derived instantaneous inter-packet times (tps) by dividing instantaneous packet sizes by video encoding rates

20. Video Encoding Rates (bsnd) Modeling
Time-series modeling of the bsnd data using the classical decomposition method
We find a Second order moving average [MA(2)] process model fit
θ1 – MA(1) parameter
θ2 – MA(2) parameter
High alev
Low alev

21. Video Packet Size (tps) Distribution Modeling
Distribution-fit analysis on the tps data
We find a Gamma distribution fit
α – shape parameter
β – scale parameter
For High alev
at 256 Kbps

22. Traffic Model Parameters for MAPTs Emulation

23. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

24. Vperf Tool Implementation of MAPTs
Per-second frequency of “Interim Test Report” generation
Interaction QoE reported by Vperf tool - based on the progress of the session-agenda

25. Example – Session Agenda and Network Factor Limits File

26. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

27. MAPTs Emulation at different Dialing Speeds
256 Kbps
384 Kbps
768 Kbps

28. MAPTs Measurements Evaluation
Increased the number of Type-I and Type-II network fault events in a controlled LAN testbed for a fixed session-agenda
NISTnet network emulator for network fault generation
Recorded Unwanted Agenda-Bandwidth and Unwanted Agenda-Time measured by Vperf tool
Impact of Type-I Network Fault Events on Unwanted Agenda-Bandwidth
(b) Impact of Type-II Network Fault Events on Unwanted Agenda-Bandwidth

29. MAPTs Measurements Evaluation (2)
(c) Impact of Type-I and Type-II Network Fault Events on Unwanted Agenda-Time

30. Outline Background Multi-Activity Packet Trains (MAPTs) methodology
Voice and Video over IP (VVoIP) Overview
Network QoS and End-user QoE in VVoIP
Streaming QoE versus Interaction QoE
Network Fault Events
Multi-Activity Packet Trains (MAPTs) methodology
Participant Interaction Patterns
Traffic Model for MAPTs emulation
Vperf tool implementation of MAPTs
Performance Evaluation
Concluding Remarks and Future Work

31. Conclusion We proposed a Multi-Activity Packet Trains methodology
Mimic participant interaction patterns and video activity levels as affected by network fault events
MAPTs provide real-time objective measurements of Interaction QoE in a large-scale videoconferencing system
Without requiring end-users, actual video sequences, VVoIP appliances
Defined new Interaction QoE Metrics
Unwanted Agenda-Bandwidth, Unwanted Agenda-Time
Implemented MAPTs in an active measurement tool called Vperf and evaluated Interaction QoE measurements on a network testbed

32. Future Work
Our work is a first-step towards measuring how network fault events impact Interaction QoE in videoconferencing sessions
We considered basic participant interaction patterns and network fault event types
Future scope could include several other participant interaction patterns and network fault event types
E.g. MAPTs for network fault events that cause lack of lip-sync
Human subject testing to more accurately map and validate network fault event types and participant interaction patterns

33. Thank you for your attention! ☺ Any Questions?