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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