1 / 18 network characteristics of video streaming traffic ashwin rao, yeon-sup lim *, chadi barakat,...
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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
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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
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Video Streaming Services
Containers
Desktop Browsers Native Mobile Applications
What are the Network Characteristics of Video Streaming Traffic?
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Objective• Network Characteristics of Video Streaming Traffic
– Causes– Impact
Outline• Introduction and Motivation• Datasets and Measurement Techniques• Streaming Strategies• Impact of Streaming Strategies
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Datasets
• YouTube videos– 5000 Flash– 3000 HTML5– 2000 HD Videos (Flash)– 50 Mobile
• Netflix videos - Silverlight– 200 to Desktop – 50 to Mobile
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Measurement Technique
802.11
Packet Capture
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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
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Outline• Introduction and Motivation• Datasets and Measurement Techniques• Streaming Strategies• Impact of Streaming Strategies
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Generic Behavior of Video StreamingD
ownl
oad
Amou
nt
Time
Buf
ferin
gBlock Size
On
Off
Steady State
Average rate
∝
Video encoding rate
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We Identified Three Streaming Strategies
No On Off Cycles
Long On Off Cycles
OFF OFF
Short On Off Cycles
Streaming strategies vastly different
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Streaming Strategies UsedService YouTube Netflix
Container Flash HD (Flash) HTML5 Silverlight
IE 9 Short No Short Short
Firefox Short No No Short
Chrome Short No Long Short
iOS (native)
- - Based on encoding rate
Short
Android (native)
- - Long Long
Streaming strategy differs withapplication type and container
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Important Features of a Strategy
• Buffering Amount• Block Size• Accumulation Ratio
Average download rate in steady state phase
Video encoding rate=
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Short ON OFF Strategy
64 kB
40 sec. of playback
Server side rate control with
absence of ACK clocks
1.25Buffering
independent ofencoding rate
Browser throttles rate
256 kB
Significant differences between implementations
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Outline• Introduction and Motivation• Datasets and Measurement Techniques• Streaming Strategies• Impact of Streaming Strategies
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Impact of Streaming StrategiesNo On Off Long On Off Short On Off
TCP Friendly Yes – TCP File Transfer
Yes – Periodic File Transfer
Unknown traffic not ack-clocked
Playout buffer occupancy
Large Moderate Small
Unused bytes on user interruptions
Large amount
Moderate amount
Small amount
StrategyMetric
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Model for Aggregate Rate of Streaming Traffic
• Objective– Capture statistical properties of aggregate
streaming trafficBarakat et al., A flow-based model for Internet backbone traffic, In IMW’02.
• Uses– Dimension the network– Quantify impact of user interruptions
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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
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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
THANK YOU
Network Characteristics of Video Streaming Traffic