1. 11/16/2018
A PhD Dissertation
Using Bandwidth Estimation to Optimize Buffer and Rate Selection for Streaming Multimedia over IEEE Wireless Networks
Mingzhe Li
Committee:
Prof. Mark Claypool – CS, Worcester Polytechnic Institute
Prof. Robert Kinicki – CS, Worcester Polytechnic Institute
Prof. Emmanuel Agu – CS, Worcester Polytechnic Institute
Prof. Constantinos Dovrolis - College of Computing, Georgia-Tech
PhD Dissertation
Computer Science Dept.
Worcester Polytechnic Institute
November 16, 2018

2. Motivation Improve streaming performance over wireless networks
11/16/2018
Motivation
Improve streaming performance over wireless networks
Increasingly deployment of streaming multimedia over wireless networks
Wireless link capacity increasing
Streaming techniques become maturing
Hardware price decreasing
Streaming techniques are not optimized for wireless networks
Streaming rate selection
Playout buffer
Bandwidth estimation
PhD Dissertation – November 16, 2018

3. Streaming Rate Selection
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Streaming Rate Selection
Goal: Adjust streaming media's data rate according to the network conditions.
High
Best
Network Bottleneck
Mid
Streaming Client
Low
Packet losses
Streaming Server
PhD Dissertation – November 16, 2018

4. Streaming Rate Selection (cont.)
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Streaming Rate Selection (cont.)
Traditional approaches
Based on packet loss rate, Round Trip Time (RTT), or application quality metrics (Frame rate/image quality).
Based on packet pair bandwidth estimation
Rate selection in wireless networks
Packet loss could be caused by bit error, but not congestion
Round trip time variation could be caused by MAC layer contention, but not queuing delay
Inaccurate bandwidth estimations
Traditional approach results in selecting a rate higher/lower than the best rate in wireless networks
PhD Dissertation – November 16, 2018

5. 11/16/2018
Playout Buffer
Goal: Smooth variation in bandwidth and streaming bit rate at the cost of startup delay.
Buffer
Network Bottleneck
Media
Streaming Client
Streaming Server
Select buffer size:
Tradeoff between buffer underflow events and startup delay
Ideally, adaptive to the network conditions
PhD Dissertation – November 16, 2018

6. Playout Buffer (cont.) Traditional approaches
11/16/2018
Playout Buffer (cont.)
Traditional approaches
Fixed size for commercial applications
Adaptive based on network measurements
Playout buffer in wireless networks
High variance in available bandwidth
Traditional adaptive buffer size is insufficient
Result in more buffer underflow events in wireless networks or long startup delay
PhD Dissertation – November 16, 2018

7. Bandwidth Estimation Traditional approaches
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Bandwidth Estimation
Traditional approaches
Designed for precisely estimate the bandwidth in wired networks
Converge based on searching algorithms
Provide limited bandwidth information
Impacted by wireless networks
Inaccurate results
Long estimation time
High intrusiveness
PhD Dissertation – November 16, 2018

8. 11/16/2018
Requirements for Bandwidth Estimation for Streaming over Wireless Networks
Adequate accuracy in bandwidth estimation for wireless networks
Fast estimation time
Low intrusiveness
Broader bandwidth information
Rate selection needs available bandwidth
Playout buffer size selection needs variance in bandwidth
PhD Dissertation – November 16, 2018

9. Dissertation Block Diagram
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Dissertation Block Diagram
PhD Dissertation – November 16, 2018

10. Dissertation Components
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Dissertation Components
Dissertation Components
Approach
Wireless Bandwidth Estimation Tool (WBest)
Buffer and Rate Optimization for Streaming (BROS)
Emulated Streaming (EmuS) M A S I P M A I P I P
M: Analytical Model
A: Optimization Algorithm
S: Simulation
I: Implementation
P: Performance Evaluation
PhD Dissertation – November 16, 2018

11. Outline Introduction The Dissertation
11/16/2018
Outline
Introduction
The Dissertation
Wireless Bandwidth Estimation Tool (WBest)
Related Work
Algorithm
Evaluation
Contributions
Future work
PhD Dissertation – November 16, 2018

12. Related Bandwidth Estimation Techniques
11/16/2018
Related Bandwidth Estimation Techniques
Packet dispersion
bprobe [Crovella 1996], pathrate [Dovrolis, 2001], etc
Measure the packet pair/train dispersion to estimate the bottleneck capacity
Fast, medium accuracy, low intrusiveness
Self-loading probe (Probe Rate Model - PRM)
pathChirp [Ribeiro 2003], pathload [Jain 2003], etc
Vary traffic load and use the packet delay to estimate the available bandwidth at the narrow link
Slow, high accuracy, high intrusiveness
Probe Gap Model (PGM)
Spruce [Strauss 2003], IGI [Hu 2003], etc.
Use the changes of the gap between packets to estimate the crossing traffic, then estimate the available bandwidth with given bottleneck capacity
Assume a known bottleneck capacity
PhD Dissertation – November 16, 2018

13. Packet Dispersion Bottleneck router L : Packet size
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Packet Dispersion
Bottleneck router
L : Packet size
Ci: Bottleneck capacity
∆in: Initial gap
∆out: Dispersed gap
PhD Dissertation – November 16, 2018

14. Example: Packet Dispersion with Wireless Contention
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Example: Packet Dispersion with Wireless Contention
Probing traffic
Contending traffic /
Co-channel interference
PhD Dissertation – November 16, 2018

15. Simulation Results of Packet Dispersion in Wireless Networks
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Simulation Results of Packet Dispersion in Wireless Networks
Higher and inconstant overhead
Inter frame spaces
CSMA/CA handshakes
Random delay between two back-to-back frames
Physical layer rate adaptation
Multipath fading
Signal attenuation
Bursty errors
MAC layer contention
Shared media
IEEE MAC Retry (ARQ)
Exponential backoffs
High Bit Error Rate (BER)
Lost frames
MAC layer ARQ
NS2 Simulations (V2.27):
Ideal channel
Fading channel with multirate PHY
Contending channel
Bit Error Rate (BER)
PhD Dissertation – November 16, 2018

16. Wireless Bandwidth Estimation Tool (WBest)
11/16/2018
Wireless Bandwidth Estimation Tool (WBest)
Objective:
fast, low intrusiveness, adequately accurate estimation of available bandwidth and variance of bandwidth in wireless networks
Two-step algorithm:
a packet pair technique to estimate the effective capacity of the wireless network;
a packet train technique to estimate the mean and standard deviation of available bandwidth
PhD Dissertation – November 16, 2018

17. Terminologies Effective capacity (Ce) Available bandwidth (A)
11/16/2018
Terminologies
Effective capacity (Ce)
Indicates the maximum capability of the wireless network to deliver network layer traffic
Includes rate adaptation impact
Includes the BER impact
Available bandwidth (A)
Maximum unused bandwidth
Impacted by contending/crossing traffic
A = Ce – S, where S is bandwidth reduction caused by crossing and contending traffic
PhD Dissertation – November 16, 2018

18. 11/16/2018
WBest Assumptions
Assume the last hop wireless network (hth hop) is the bottleneck link with a single FCFS queue and:
Assume no significant changes in network conditions between the two steps (estimating Ce and A)
PhD Dissertation – November 16, 2018

19. Estimating Effective Capacity (Ce)
11/16/2018
Estimating Effective Capacity (Ce)
Send n packet pairs to estimate Ce:
Ti : dispersion time of ith packet pair (seconds)
L : packet size (bytes)
Use the median of n estimations to minimize the impact of crossing and contending traffic
PhD Dissertation – November 16, 2018

20. Estimating Available Bandwidth (A)
11/16/2018
A packet train of m packets is sent at effective capacity (Ce) to estimate available bandwidth
FCFS queuing at AP
R : dispersion rate S : crossing/contending traffic
S’ : reduced crossing/contending traffic
Estimate the contending and crossing traffic (S) using the dispersion rate (R)
PhD Dissertation – November 16, 2018

21. Estimating Available Bandwidth (A) (cont.)
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Estimating Available Bandwidth (A) (cont.)
Mean available bandwidth (A)
Infer the variance of available bandwidth A
Measured R
PhD Dissertation – November 16, 2018

22. Advantages of WBest Fast estimation Low intrusiveness
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Advantages of WBest
Fast estimation
Not based on convergence searching algorithm
Low intrusiveness
Reasonably accurate bandwidth estimation
Reduce the wireless impact on searching algorithm
Delay measurement
Wireless losses
Others
Estimation of effective capacity Ce
Estimation of variance in available bandwidth
PhD Dissertation – November 16, 2018

23. Evaluation Build testbed Compare with: Open source drivers
11/16/2018
Build testbed
Open source drivers
Wireless sniffer
Various wireless conditions
Traffic load
Power saving mode
Rate adaptation
Implementation of WBest
Compare with:
IGI/PTR v2.0 [Hu 2003] (PGM/PRM)
pathChirp v2.4.1 [Ribeiro 2003] (PRM)
pathload v1.3.2 [Jain 2003]
(PRM)
PhD Dissertation – November 16, 2018

24. Evaluation (cont.) 15 evaluation cases Repeat 30 times for each case
11/16/2018
Evaluation (cont.)
15 evaluation cases
Various Crossing/Contending traffic
TCP and UDP protocols
Rate adaptation
Power saving mode
Repeat 30 times for each case
Each run consists of four tools and one Constant Bit Rate (CBR) to measure the “ground truth” of available bandwidth
In a lab with radio-shield paint, during middle of night
Performance metrics
Available bandwidth (Mbps)
Estimation time /convergence time (seconds)
Intrusiveness (MBytes)
PhD Dissertation – November 16, 2018

25. Results of Available Bandwidth (UDP crossing traffic)
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Results of Available Bandwidth (UDP crossing traffic)
Ground truth
Ground Truth
PhD Dissertation – November 16, 2018

26. Results of Estimation Time (UDP crossing traffic)
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Results of Estimation Time (UDP crossing traffic)
PhD Dissertation – November 16, 2018

27. Results of Intrusiveness (UDP crossing traffic)
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Results of Intrusiveness (UDP crossing traffic)
PhD Dissertation – November 16, 2018

28. Summary of all 15 Cases (Estimation Times)
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Summary of all 15 Cases (Estimation Times)
Error is different between estimated bandwidth and the “ground truth”
measured by CBR throughput.
PhD Dissertation – November 16, 2018

29. Summary of all 15 Cases (Intrusiveness)
11/16/2018
Summary of all 15 Cases (Intrusiveness)
PhD Dissertation – November 16, 2018

30. Outline Introduction The Dissertation
11/16/2018
Outline
Introduction
The Dissertation
Wireless Bandwidth Estimation Tool (WBest)
Related Work
Algorithm
Evaluation
Contributions
Future work
PhD Dissertation – November 16, 2018

31. Contributions Wireless Bandwidth Estimation Tool (WBest)
11/16/2018
Wireless Bandwidth Estimation Tool (WBest)
Fast, low intrusiveness, accurate estimation for IEEE networks
Broader bandwidth information: Effective capacity, Available bandwidth and variance
Implemented and evaluated in Linux
Packet dispersion model in wireless networks
Includes channel rate, contending, BERs, MAC layer retry and exponential backoff
Validated by simulations and measurements
PhD Dissertation – November 16, 2018

32. Contributions (cont.) Playout buffer model
11/16/2018
Contributions (cont.)
Playout buffer model
Markov Chain model based on streaming rate and distribution of available bandwidth
Validated by measurements in wireless networks
PhD Dissertation – November 16, 2018

33. 11/16/2018
Contributions (cont.)
Buffer and Rate Optimization for Streaming (BROS)
Optimizes streaming rate and initial buffer size based on WBest estimations
Adjustable based on target buffer underflow probability
Implemented and evaluated in wireless networks
Reduce buffer underflow by nearly 100%
Reduce frame loss by nearly 100%
Reduce total buffer delay by 80%
Emulated Streaming (EmuS) system
Multiple encoded layers and configurable playout buffer
Provides performance information: frame loss, buffer delay and underflow
Integrated with WBest and BROS
PhD Dissertation – November 16, 2018

34. 11/16/2018
Future Work
Extend WBest to other types of wireless networks such as WWANs
Extend BROS/WBest for live or interactive applications
Improve WBest to use streaming multimedia data to estimate available bandwidth
Improve WBest to report loss rate and delay information, which may be used to improve the media repair techniques
PhD Dissertation – November 16, 2018

35. Acknowledgements Prof. Claypool and Prof. Kinicki Prof. Agu
11/16/2018
Acknowledgements
Prof. Claypool and Prof. Kinicki
Prof. Agu
Prof. Dovrolis from Georgia-Tech
Faculty/Staff of Computer Science Dept., WPI
Jae Chung, Feng Li, Rui Lu, Hao Shang, Huahui Wu, and everyone from PEDS and CC groups
Attendees today
My Family
PhD Dissertation – November 16, 2018

36. 11/16/2018
A PhD Dissertation
Using Bandwidth Estimation to Optimize Buffer and Rate Selection for Streaming Multimedia over IEEE Wireless Networks
Mingzhe Li
Committee:
Prof. Mark Claypool – CS, Worcester Polytechnic Institute
Prof. Robert Kinicki – CS, Worcester Polytechnic Institute
Prof. Emmanuel Agu – CS, Worcester Polytechnic Institute
Prof. Constantinos Dovrolis - College of Computing, Georgia-Tech
PhD Dissertation
Computer Science Dept.
Worcester Polytechnic Institute
November 16, 2018

37. Rate Selection in IEEE 802.11g WLAN
11/16/2018
Rate Selection in IEEE g WLAN
2-minute Windows Media streaming session
Streaming over IEEE g WPI campus WLAN
11 encoding levels
PhD Dissertation – November 16, 2018

38. Buffer Events in IEEE 802.11g WLAN
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Buffer Events in IEEE g WLAN
PhD Dissertation – November 16, 2018

39. 11/16/2018
BROS rate selection
PhD Dissertation – November 16, 2018

40. BROS buffer optimization
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BROS buffer optimization
PhD Dissertation – November 16, 2018

41. Buffer Underflow (Rate adaptation case)
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Buffer Underflow (Rate adaptation case)
PhD Dissertation – November 16, 2018

42. Frame Loss (Rate adaptation case)
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Frame Loss (Rate adaptation case)
PhD Dissertation – November 16, 2018

43. Total Buffer Delay (Rate adaptation case)
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Total Buffer Delay (Rate adaptation case)
PhD Dissertation – November 16, 2018