Mobile Video Deliveryvia Human Movement

Gene Moo Lee, Swati Rallapalli, Wei Dong, Yi-Chao Chen, Lili Qiu, Yin Zhang

University of Texas at Austin

SECON 2013, New Orleans LA

Introduction● Mobile devices have great local connectivities

○ WiFi, NFC, Bluetooth● People bring mobile devices wherever they go

○ Home, workplace, restaurants, subways, bus stops

Can we leverage human movements as a content distribution channel?

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0.65 Mbps (in our LBSN data)

VideoFountain● VideoFountain is a mobile content distribution system

○ To deploy kiosks (WiFi AP w/ storages) at popular venues○ To let mobile users download/upload contents○ To leverage mobile users to deliver contents between kiosks

● Challenges○ To understand real user mobility○ To bootstrap mobile contents○ To route contents○ To incentivize users○ To protect copyrights○ To preserve integrity of contents○ so on...

source: http://www.redbox.com

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Roadmap

1. User mobility analysisa. Location-based social networksb. Various mobility properties

2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain

3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors

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Mobility analysis with LBSN● LBSN: Location-Based Social Networks

○ Mobile users check-in to places where they visit○ Foursquare, Gowalla, Facebook, Google+

● Why LBSN?○ Mobile devices are the targets of VideoFountain○ Dwell time is long enough to do manual check-in○ Massive data to understand human movement

● Limitations○ No check-out time, missing check-ins

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Venue popularity

● Popularity of venues (# of check-ins) follows Zipf-like distribution○ Significant value of placing content

at popular venues

● Venue popularity is stable over time○ We can learn from historical data

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Number of check-ins

Inter-venue human traffic● Aggregated human traffic between venues

○ T(a,b,t) = # users moving from venue a to venue b in day t■ user u check-ins at a then b

● Human traffic is stable over time○ In two week data, T(a,b,t) doesn't change for 80-90% pairs

● Aggregated human traffic exhibits Zipf-like distribution

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Number of human traffic

Degree of separation

● How well connected are the venues in a city?○ 80-90% venue pairs are within 2-3 hops○ Routing is relatively easy

● Methodology

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Number of hops

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enue

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Individual mobility prediction

● Given a user is at venue X, can we predict the next check-in venue?○ Somewhat. ○ Paris (55%), Manhattan (41%), Austin (26%)

● Use a very simple algorithm to predict user's next-checkin based on personal history plus movements from general population○ See paper for algorithm details

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Roadmap

1. User mobility analysisa. Location-based social networksb. Various mobility properties

2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain

3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors

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Initial content placement

● Given the set of destinations to deliver contents, where do we seed the contents?

● Placement algorithms○ Popularity-based placement

■ Place the contents from the most popular venues○ Utility-based placement

■ Place the contents from the venues that maximize our utility functions● Ex. Minimize the distances to the destinations

■ Use greedy algorithm○ Random placement

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Routing algorithms

How to route initial contents to destination venues?

● Utility-based replication○ When a user visits a venue, calculate the marginal utilities of

uploading (user->venue) or downloading (venue->user) contents

○ Execute download/upload with highest marginal utility

○ Marginal utility: an example with geographic distance■ How much the content is getting closer to the final

destination with this download/upload?

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Utility functions

Let Sc be the set of venues that currently have the content cLet dc be the destination venue of content c

● Expected delay○ What is the expected delay from Sc to dc ?

● Geographic distance○ How far is the Sc to dc ?

● Single-hop traffic○ How many people move from Sc to dc in a day?

● Multi-hop traffic○ Consider multi-hop traffic with decay factor

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Benchmark algorithms

● Flooding○ Whenever a user visits a venue, copy all the contents

● Epidemic routing○ Flooding with hop count limit (2 in the experiments)

● Oracle routing (upperbound)○ Assume that we know the users' next check-ins○ Construct spatio-temporal graph○ Run LP to optimize the throughput achievable in the graph

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Roadmap

1. User mobility analysisa. Location-based social networksb. Various mobility properties

2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain

3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors

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Trace-driven simulation

● Foursquare traces○ Train: January 2nd 2012 to January 16th 2012 (2 weeks)○ Test: January 17th 2012 to January 30th 2012 (2 weeks)

● Default settings○ Wireless capacity: 50 Mbps (802.11n)○ Storage: 10 GB (mobile), 1 TB (venue)○ Dwell time: exponential distribution with mean 60 mins○ 50 flows generation (1GB each)

■ Sources: 1% or 5% venues (by placement algorithms)■ Destinations: up to 10% random venues (non-source)

● Evaluation metrics○ Flow delivery rate: how many flows completely delivered○ Traffic delivery rate: consider partial delivery

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Impact of initial placement

● Utility based placement outperforms others○ Random 25%, popular 67%, utility 74%

● Utility based routing outperforms others○ Different utility functions work for different cities

● Utility routing vs Oracle routing○ Paris: 69% vs 81%○ London: 69% vs 84%

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Random placement Utility placement

Flow

del

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y (%

)

Flow

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Impact of contents

● Results from London○ Consistent in other cities

● Number of flows○ Increasing congestion○ Utility routing degrades

gracefully○ Flooding/Epidemic suffer

● Content sizes○ The system can support 500+

MB contents, which can cover most mobile contents

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Impact of device specs

● Wireless capacity○ The higher bandwidth, the better

system performs○ 50 Mbps is enough

● User storage○ The larger storage, the better

system performs○ 10GB is enough

● Replacement strategy○ Utility-based outperforms others

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Impact of user behaviors

● User dwell time○ Avg 30 mins is enough

● Comparing Oracle vs Prediction○ Difference within 18%~38%

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Conclusion

● Collected and analyzed LBSN data (Foursquare, Gowalla)○ Venue popularity and human traffic exhibit Zipf-like distribution○ Cities are well-connected by human movements

● Proposed VideoFountain○ Mobile content distribution system leveraging human movements○ Designed placement & routing algorithms

● Evaluated the system with trace-driven simulations○ Utility-based placement & routing work well the best

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Thank you!

Link capacity between venues

● Does inter-venue human movement link make considerable bandwidth?○ Yes, it is.

● A simple back-of-the-envelope calculation:○ Assume WiFi bandwidth L (50 Mbps)○ If user u stays at venue a for ta then venue b for tb,

■ then the user can carry min(ta, tb) X L / 2■ assuming she downloads and uploads equally

○ Latency T: inter-checkin time from a to b

● Avg capacity of all inter-venue movements = 0.65 Mbps○ Avg global internet speed = 2.9 Mbps○ Avg US internet speed = 7.4 Mbps○ source : https://www.cabletechtalk.com/broadband-internet/u-s-moves-up-in-average-worldwide-internet-speed-

rankings/

Back to main slides

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Spatio-Temporal Graph● Construct spatio-temporal graph

○ Two venues are linked if any user checked two venues in that day

○ optimistic vs conservative

Back to main slides

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Venue popularity● Popularity of venues (# of

checkins) follows Zipf-like distribution○ Richer gets richer○ Significant value of placing

content at popular venues

● Venue popularity is stable over time○ We can learn it from historical

data

● Popular venues are spread across the city○ pair-wise distances

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Benchmark algorithms

● Flooding○ Whenever a user visits a venue, copy all the contents

● Epidemic routing○ Flooding with hop count limit (2 in the experiments)

● Oracle routing (upperbound)○ Assume that we know the users' next checkins○ Construct spatio-temporal graph○ Run LP to optimize the throughput achievable in the graph

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Impact of initial placement

● Seeds: 1% and 5% venues

● 1% seed results:○ Random placement: 23%○ Popular placement: 77%○ Utility placement: 64%

● 5% seed results:○ Random placement: 25%○ Popular placement: 67%○ Utility placement: 74%

● Utility routing vs Oracle routing○ Paris: 69% vs 81%○ London: 69% vs 84%

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Introduction● People bring mobile devices wherever they go

○ Home, workplace, restaurants, subways, bus stops● Mobile devices have great local connectivities

○ WiFi, NFC, Bluetooth

Can we leverage human movements as a content distribution channel?

source: http://www.apple.com/ios/ios7/features/#airdrop

source: http://www.androidcentral.com/hands-s-beam-samsung-galaxy-s-iii

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