Network Characteristics of Video Streaming Traffic

1. Network Characteristics of Video Streaming Traffic Ashwin Rao†, Yeon-sup Lim*, Chadi Barakat†, Arnaud Legout†, Don Towsley*, and Walid Dabbous† *University of Massachusetts  Amherst, USA †INRIA Sophia Antipolis, France

2. Video Streaming in the Internet • 20 % to 40 % of all Internet traffic • Traffic share steadily increasing in recent years • Streaming over HTTP – using TCP • Firewall configurations • TCP flows assumed to be fair

3. Video Streaming Services Containers What are the Network Characteristics of Video Streaming Traffic?

4. Objective • Network Characteristics of Video Streaming Traffic • Causes • Impact Outline • Introduction and Motivation • Datasets and Measurement Techniques • Streaming Strategies • Impact of Streaming Strategies

5. Datasets • YouTube videos • 5000 Flash • 3000 HTML5 • 2000 HD Videos (Flash) • 50 Mobile • Netflix videos - Silverlight • 200 to Desktop • 50 to Mobile

6. Measurement Technique Packet Capture 802.11

7. Measurement Locations • France • Academic (Wired; Wi-fi for mobile) • Residential (Wi-fi) • USA • Academic (Wired; Wi-fi for mobile) • Residential (Wired) YouTube Similar Traffic Characteristics at Each Location YouTube and Netflix

8. Outline • Introduction and Motivation • Datasets and Measurement Techniques • Streaming Strategies • Impact of Streaming Strategies

9. Generic Behavior of Video Streaming Steady State Off On Average rate ∝ Video encoding rate Block Size Download Amount Buffering Time

10. We Identified Three Streaming Strategies No On Off Cycles Streaming strategies vastly different Short On Off Cycles Long On Off Cycles OFF OFF

11. Streaming Strategies Used Streaming strategy differs with application type and container

12. Important Features of a Strategy • Buffering Amount • Block Size • Accumulation Ratio Average download rate in steady state phase = Video encoding rate

13. Short ON OFF Strategy 1.25 64 kB Buffering independent of encoding rate 40 sec. of playback Significant differences between implementations Browser throttles rate Server side rate control with absence of ACK clocks 256 kB

14. Outline • Introduction and Motivation • Datasets and Measurement Techniques • Streaming Strategies • Impact of Streaming Strategies

15. Impact of Streaming Strategies Strategy Metric

16. Model for Aggregate Rate of Streaming Traffic • Objective • Capture statistical properties of aggregate streaming traffic Barakat et al., A flow-based model for Internet backbone traffic, In IMW’02. • Uses • Dimension the network • Quantify impact of user interruptions

17. Insights from Model • No User Interruptions • Aggregate rate (mean, variance, etc.) independent of streaming strategy • Dimensioning rules do not change • Strategy to optimize other goals (server load, etc.) • Users Interruptions • Impact of buffering amount and accumulation ratio on wasted bandwidth

18. Conclusion • Most popular clients and containers for video streaming • Streaming strategy differs with client applications and container • HTML5 streaming vastly differs with client applications • Model to study impact of streaming strategies

19. Network Characteristics of Video Streaming Traffic THANK YOU