TANGO: Reliable Mobile Streaming Cooperation
T
ANGO
: Toward a More Reliable Mobile
Streaming through Cooperation between
Cellular Network and Mobile Devices
Nawanol Theera-Ampornpunt, Tarun Mangla,
Saurabh Bagchi, Rajesh Panta, Kaustubh Joshi,
Mostafa Ammar, and Ellen Zegura
Purdue University, Georgia Institute of
Technology, AT&T Labs Research
Motivation
•
Mobile devices treat cellular network as black
box
2
Application Server
Mobile Device
Cellular Network
Audio Streaming
•
Pandora model – songs are chosen by the
service
•
Online audio streaming where next songs are
known
3
Buffer Size Tradeoff
•
Large buffer -> more resilient to congestion
•
Small buffer -> lower bandwidth waste in case
user abandons the stream
•
Ideal: small buffer when connectivity is good;
large buffer when congestion is expected
4
Data Pre-caching Service
•
Runs inside cellular network
•
Monitors user’s movement trajectory
•
Sends an alert to streaming application when
user is predicted to enter a congested area
•
Application significantly increases buffer size
to mitigate effect of congestion
5
Overview
•
Offline phase
–
Mobility prediction model training
•
Online phase
–
User location prediction
–
Network load monitoring
6
Mobility Prediction Model
•
Operates at cell sector level
•
Estimates probability of entering cell C in the
next
u
minutes given past trajectory
•
Based on simple conditional probability:
P(enter cell C | trajectory) =
Freq(enter cell C, trajectory) / Freq(trajectory)
•
Gets counts for each unique trajectory from
trace data
7
Mobility Prediction Accuracy
8
Trace-driven Simulations
•
We rely on simulations to estimate benefits of
pre-caching service for large number of users
•
Audio streaming client emulator keeps track of
current song position, buffer level, user
location, etc.
•
Emulated cells have fixed capacity, with
background traffic from real traces
9
Simulated Congestion
•
Simulated congested cells have capacity of
zero
•
Three simulations, each with one type of
congestion:
–
Static congestions – congestion in 20% of cells for
the whole duration
–
Random congestions – congestion in 50% of cells
lasting 0-20 minutes
–
Flash crowds – 50 congestions that move like a
user
10
Approaches Compared
•
Baseline: fixed buffer size and no bit-rate
adaptation
•
MPEG-DASH: fixed buffer size w/ bit-rate
adaptation
•
Tango
: dynamic buffer size
–
w/ and w/o bit-rate adaptation
–
w/ and w/o perfect location predictor
11
Results – Pause Time (1)
12
Results – Pause Time (2)
13
Results – Pause Time (3)
14
Results – Average Stream Bit-rate
15
Results – Buffer size vs. Pause Time
16
Conclusion
•
We propose
Tango
, a framework that enables
cooperation between mobile devices and
cellular network
•
We introduce data pre-caching service that
notifies streaming application of impending
congestion
•
Trace-based simulations show service reduces
pause time by 13-72% depending congestion
type and number of users
17
Questions?
18
This study delves into enhancing mobile streaming reliability through collaboration between cellular networks and mobile devices, proposing innovative solutions like data pre-caching services and mobility prediction models. By optimizing buffer sizes and leveraging network intelligence, the aim is to create a more seamless streaming experience for users. Trace-driven simulations are employed to evaluate the efficacy of these strategies in real-world scenarios.
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Presentation Transcript
TANGO: Toward a More Reliable Mobile Streaming through Cooperation between Cellular Network and Mobile Devices Nawanol Theera-Ampornpunt, Tarun Mangla, Saurabh Bagchi, Rajesh Panta, Kaustubh Joshi, Mostafa Ammar, and Ellen Zegura Purdue University, Georgia Institute of Technology, AT&T Labs Research
Motivation Mobile devices treat cellular network as black box Communicate Mobile Device Application Server Cellular Network 2
Audio Streaming Pandora model songs are chosen by the service Online audio streaming where next songs are known 3
Buffer Size Tradeoff Large buffer -> more resilient to congestion Small buffer -> lower bandwidth waste in case user abandons the stream Ideal: small buffer when connectivity is good; large buffer when congestion is expected 4
Data Pre-caching Service Runs inside cellular network Monitors user s movement trajectory Sends an alert to streaming application when user is predicted to enter a congested area Application significantly increases buffer size to mitigate effect of congestion 5
Overview Offline phase Mobility prediction model training Online phase User location prediction Network load monitoring Legend Online Phase Offline Phase 6
Mobility Prediction Model Operates at cell sector level Estimates probability of entering cell C in the next u minutes given past trajectory Based on simple conditional probability: P(enter cell C | trajectory) = Freq(enter cell C, trajectory) / Freq(trajectory) Gets counts for each unique trajectory from trace data 7
Mobility Prediction Accuracy (number of past cells in trajectory) 8
Trace-driven Simulations We rely on simulations to estimate benefits of pre-caching service for large number of users Audio streaming client emulator keeps track of current song position, buffer level, user location, etc. Emulated cells have fixed capacity, with background traffic from real traces 9
Simulated Congestion Simulated congested cells have capacity of zero Three simulations, each with one type of congestion: Static congestions congestion in 20% of cells for the whole duration Random congestions congestion in 50% of cells lasting 0-20 minutes Flash crowds 50 congestions that move like a user 10
Approaches Compared Baseline: fixed buffer size and no bit-rate adaptation MPEG-DASH: fixed buffer size w/ bit-rate adaptation TANGO: dynamic buffer size w/ and w/o bit-rate adaptation w/ and w/o perfect location predictor 11
Conclusion We propose TANGO, a framework that enables cooperation between mobile devices and cellular network We introduce data pre-caching service that notifies streaming application of impending congestion Trace-based simulations show service reduces pause time by 13-72% depending congestion type and number of users 17
Questions? 18
