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1 / 18 Network Characteristics of Video Streaming Traffic Ashwin Rao, Yeon-sup Lim *, Chadi Barakat, Arnaud Legout, Don Towsley *, and Walid Dabbous INRIA.

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Presentation on theme: "1 / 18 Network Characteristics of Video Streaming Traffic Ashwin Rao, Yeon-sup Lim *, Chadi Barakat, Arnaud Legout, Don Towsley *, and Walid Dabbous INRIA."— Presentation transcript:

1 1 / 18 Network Characteristics of Video Streaming Traffic Ashwin Rao, Yeon-sup Lim *, Chadi Barakat, Arnaud Legout, Don Towsley *, and Walid Dabbous INRIA Sophia Antipolis, France * University of Massachusetts Amherst, USA

2 2 / 18 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 3 / 18 Video Streaming Services Containers Desktop BrowsersNative Mobile Applications What are the Network Characteristics of Video Streaming Traffic?

4 4 / 18 Objective Network Characteristics of Video Streaming Traffic –Causes –Impact Outline Introduction and Motivation Datasets and Measurement Techniques Streaming Strategies Impact of Streaming Strategies

5 5 / 18 Datasets YouTube videos –5000 Flash –3000 HTML5 –2000 HD Videos (Flash) –50 Mobile Netflix videos - Silverlight –200 to Desktop –50 to Mobile

6 6 / 18 Measurement Technique Packet Capture

7 7 / 18 Measurement Locations France –Academic (Wired; Wi-fi for mobile) –Residential (Wi-fi) USA –Academic (Wired; Wi-fi for mobile) –Residential (Wired) YouTube YouTube and Netflix Similar Traffic Characteristics at Each Location

8 8 / 18 Outline Introduction and Motivation Datasets and Measurement Techniques Streaming Strategies Impact of Streaming Strategies

9 9 / 18 Generic Behavior of Video Streaming Download Amount Time Buffering Block Size On Off Steady State Average rate Video encoding rate

10 10 / 18 We Identified Three Streaming Strategies No On Off Cycles Long On Off Cycles OFF Short On Off Cycles Streaming strategies vastly different

11 11 / 18 Streaming Strategies Used ServiceYouTubeNetflix ContainerFlashHD (Flash)HTML5Silverlight IE 9ShortNoShort FirefoxShortNo Short ChromeShortNoLongShort iOS (native) --Based on encoding rate Short Android (native) --Long Streaming strategy differs with application type and container

12 12 / 18 Important Features of a Strategy Buffering Amount Block Size Accumulation Ratio Average download rate in steady state phase Video encoding rate =

13 13 / 18 Short ON OFF Strategy 64 kB 40 sec. of playback Server side rate control with absence of ACK clocks 1.25 Buffering independent of encoding rate Browser throttles rate 256 kB Significant differences between implementations

14 14 / 18 Outline Introduction and Motivation Datasets and Measurement Techniques Streaming Strategies Impact of Streaming Strategies

15 15 / 18 Impact of Streaming Strategies No On OffLong On OffShort On Off TCP FriendlyYes – TCP File Transfer Yes – Periodic File Transfer Unknown traffic not ack-clocked Playout buffer occupancy LargeModerateSmall Unused bytes on user interruptions Large amount Moderate amount Small amount Strategy Metric

16 16 / 18 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 IMW02. Uses –Dimension the network –Quantify impact of user interruptions

17 17 / 18 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 18 / 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 THANK YOU Network Characteristics of Video Streaming Traffic


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