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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.

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

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  1. “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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Weather Prediction Figure 4: Cumulative Distribution Function (CDF) of Average Frame Rate NOSSDAV 2005 June 13, 2005

  11. 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

  12. Predictor Analysis Figure 2: Average Wireless Capacity versus RSSI NOSSDAV 2005 June 13, 2005

  13. Predictor Analysis Figure 3: Upstream MAC Layer Retry Fraction versus RSSI NOSSDAV 2005 June 13, 2005

  14. 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

  15. 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

  16. 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

  17. Weather Map AnalysisFigure 5: Frame Rate Prediction by RSSI NOSSDAV 2005 June 13, 2005

  18. Weather Map AnalysisFigure 6: Wireless Link Capacity E = 0.97 NOSSDAV 2005 June 13, 2005

  19. Coefficient of Variation of Wireless Link Capacity Figure 7 Versus Average Frame Rate Figure 8 Versus Average Link Capacity NOSSDAV 2005 June 13, 2005

  20. 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

  21. RTT Weather Maps TCP Streaming Videos E = 0.83 UDP Streaming Videos E = 0.94 NOSSDAV 2005 June 13, 2005

  22. Throughput Weather Maps Single Level Encoded Videos E = 0.82 Multiple Level Encoded Videos E = 0.31 NOSSDAV 2005 June 13, 2005

  23. Throughput Analysis Multiple Level TCP Streaming Multiple Level UDP Streaming Single Level TCP Streaming Single Level UDP Streaming NOSSDAV 2005 June 13, 2005

  24. Effectiveness Summary Table 3: Effectiveness of Weather Maps NOSSDAV 2005 June 13, 2005

  25. 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

  26. 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

  27. 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 rek@cs.wpi.edu NOSSDAV 2005 Skamania, Washington June 13-14, 2005

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