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“Weather Forecasting” Predicting Performance for Streaming Video over Wireless LANs. Mingzhe Li, Feng Li, Mark Claypool, Bob Kinicki WPI Computer Science Department Worcester, Massachusetts 01609. Presenter - Bob Kinicki. NOSSDAV 2005 Skamania, Washington June 13-14, 2005. Outline.

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weather forecasting predicting performance for streaming video over wireless lans

“Weather Forecasting”Predicting Performance for Streaming Video over Wireless LANs

Mingzhe Li, Feng Li, Mark Claypool, Bob Kinicki

WPI Computer Science Department

Worcester, Massachusetts 01609

Presenter - Bob Kinicki

NOSSDAV 2005

Skamania, Washington

June 13-14, 2005

outline
Outline
  • Motivation
  • Experiments
    • Tools and Setup
    • Experimental Design
  • Weather Forecasting
    • Weather Prediction
    • Weather Predictor
    • Weather Maps
    • Effectiveness (E)
  • Results and Analysis
  • Conclusions and Future Work

NOSSDAV 2005 June 13, 2005

motivation
Motivation
  • Increasing deployment of streaming multimedia over wireless networks.
    • The promise of higher wireless link capacities (e.g. 54 Mbps with 802.11g)
  • Streaming applications may encounter bad wireless LAN (WLAN) reception quality due to:
    • Attenuation, fading, frame collisions, rate adaptation
    • Contention, MAC layer retries
  • A Streaming User’s Question:
    • Can I get good performance here?
  • The Streaming Application’s Decision:
    • When should I do media scaling?

The answer: Provide Performance Predictions

NOSSDAV 2005 June 13, 2005

outline1
Outline
  • Motivation
  • Experiments
    • Tools and Setup
    • Experimental Design
  • Weather Forecasting
    • Weather Prediction
    • Weather Predictor
    • Weather Maps
    • Effectiveness (E)
  • Results and Analysis
  • Conclusions and Future Work

NOSSDAV 2005 June 13, 2005

tools and setup
Tools and Setup
  • Single-level streaming encoded at 2.5 Mbps
  • Multi-level streaming with 11 encoding

levels with maximum level 2.5Mbps

  • TCP
  • UDP

IEEE 802.11g at 54Mbps

Measurement Tools

NOSSDAV 2005 June 13, 2005

experimental design
Experimental Design
  • Gauging measurement tool interference
    • Baseline experiment: CPU usage < 3%
    • During measurement: CPU usage about 35%
  • Measurement locations
    • Fuller Labs: Sub Basement, 1st Floor, 3rd Floor
  • Wireless Link conditions
    • Good, Fair, Bad
  • Number of experiments
    • 2 video clips * 2 protocols * 2 encoded methods * 3 locations * 3 conditions * 5 times - {10 Bad runs thrown out}= 350 stream runs
  • Experimental period
    • Winter Break: Dec 23-25, Dec 28-29, 2004.

NOSSDAV 2005 June 13, 2005

outline2
Outline
  • Motivation
  • Experiments
    • Tools and Setup
    • Experiment Design
  • Weather Forecasting
    • Weather Prediction
    • Weather Predictors
    • Weather Maps
    • Effectiveness (E)
  • Results and Analysis
  • Conclusions and Future Work

NOSSDAV 2005 June 13, 2005

weather forecasting
Weather Forecasting

Sky conditions

http://www.astro.washington.edu/WWWgifs/weather.gif

Probability of snow

National Weather Service   http://www.nws.noaa.gov/

NOSSDAV 2005 June 13, 2005

weather prediction
Weather Prediction
  • Potential Weather predictions:
    • Average frame rate
    • Coefficient of Variation (CoV) of frame rate
    • Others
      • re-buffer count, buffering time, etc.
  • Video Frame Rate Quality Categories
    • Good (Sunny): > 24 fps
    • Edge (Cloudy): 15-24 fps
    • Bad (Rainy): < 15 fps

NOSSDAV 2005 June 13, 2005

weather prediction1
Weather Prediction

Figure 4: Cumulative Distribution Function (CDF) of Average Frame Rate

NOSSDAV 2005 June 13, 2005

weather predictors
Weather Predictors
  • Weather predictors:
    • Wireless Layer
      • Received Signal Strength Indicator (RSSI) (dBm)
      • Average Wireless Link Capacity (Mbps)
      • Wireless MAC Layer Retry Fraction (%)
    • Network Layer
      • Round Trip Time (RTT) (ms)
      • Packet Loss Rate (%)
    • Application Layer
      • Throughput (Mbps)

NOSSDAV 2005 June 13, 2005

predictor analysis
Predictor Analysis

Figure 2: Average Wireless Capacity versus RSSI

NOSSDAV 2005 June 13, 2005

predictor analysis1
Predictor Analysis

Figure 3: Upstream MAC Layer Retry Fraction versus RSSI

NOSSDAV 2005 June 13, 2005

weather maps
Weather Maps
  • Creating a Weather Map
    • Divide prediction:
      • Good (Sunny), Edge (Cloudy) and Bad (Rainy).
    • Put the predictor samples in increasing order.
    • Compute prediction probabilities.
      • Divide the predictor data into 10 “equally-populated” bins.
      • Determine the fraction of Good, Edge and Bad per bin.
    • Draw the weather map.

NOSSDAV 2005 June 13, 2005

effectiveness e
Effectiveness (E)
  • Effectiveness (E):
    • The fraction of the range of the weather predictor in a weather map that is likely to produce an accurate prediction.
    • TheEffective Range, Reffective,, is the range of a predictor that provides better than a 50% chance of yielding a good or bad prediction.
    • The Practical Range, Rall , is the useable predictor range running from the median of the first sample bin to the median of last sample bin.
      • Thus, 5 outliers are removed from both ends of the range to yield the practical range.
    • E is between 0 and 1.

NOSSDAV 2005 June 13, 2005

outline3
Outline
  • Motivation
  • Experiments
    • Tools and Setup
    • Experiment Design
  • Weather Forecasting
    • Weather Predictor
    • Weather Prediction
    • Weather Maps
    • Effectiveness (E)
  • Results and Analysis
  • Conclusions and Future Work

NOSSDAV 2005 June 13, 2005

coefficient of variation of wireless link capacity
Coefficient of Variation of Wireless Link Capacity

Figure 7

Versus

Average Frame

Rate

Figure 8

Versus

Average Link

Capacity

NOSSDAV 2005 June 13, 2005

more weather maps
More Weather Maps

Figure 9

Upstream Wireless

Retry Ratio

E = 0.75

Figure 10

IP Packet Loss

Rate

E = 0.71

NOSSDAV 2005 June 13, 2005

rtt weather maps
RTT Weather Maps

TCP Streaming

Videos

E = 0.83

UDP Streaming

Videos

E = 0.94

NOSSDAV 2005 June 13, 2005

throughput weather maps
Throughput Weather Maps

Single Level

Encoded Videos

E = 0.82

Multiple Level

Encoded Videos

E = 0.31

NOSSDAV 2005 June 13, 2005

throughput analysis
Throughput Analysis

Multiple Level TCP Streaming

Multiple Level UDP Streaming

Single Level TCP Streaming

Single Level UDP Streaming

NOSSDAV 2005 June 13, 2005

effectiveness summary
Effectiveness Summary

Table 3: Effectiveness of Weather Maps

NOSSDAV 2005 June 13, 2005

outline4
Outline
  • Motivation
  • Experiments
    • Tools and Setup
    • Experiment Design
  • Weather Forecasting
    • Weather Predictor
    • Weather Prediction
    • Weather Maps
    • Effectiveness (E)
  • Results and Analysis
  • Conclusions and Future Work

NOSSDAV 2005 June 13, 2005

conclusions and future work
Conclusions and Future Work
  • Reliable performance forecast Predictors:
    • Wireless RSSI
    • Average wireless link capacity
  • Regional predictors
    • IP loss rate < 2%
    • RTT < 10 ms
  • Effectiveness
    • varies for different video configurations.
    • Single level video performance is easy to predict.
  • Reliably forecasts of streaming “weather” can benefit video rate adaptation techniques.
  • Future Work
    • Incorporate prediction into a dynamic video system.
    • Evaluate prediction with combined weather predictors.
    • Consider weather maps with different predictions.

NOSSDAV 2005 June 13, 2005

weather forecasting predicting performance for streaming video over wireless lans1

Weather ForecastingPredicting Performance for Streaming Video over Wireless LANs

Thanks!

Mingzhe Li, Feng Li, Mark Claypool, Bob Kinicki

WPI Computer Science Department

Worcester, Massachusetts 01609

[email protected]

NOSSDAV 2005

Skamania, Washington

June 13-14, 2005

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