On Statistical Multiplexing of VBR
Video Streams in Mobile Systems
Cheng-Hsin Hsu
Simon Fraser University, Canada
joint work with Mohamed Hefeeda
ACM Multimedia 09’, October 21st, 2009
Mobile Video Broadcast Networks


Base station concurrently transmits multiple video
streams over a broadcast network to mobile devices
Design goals of base stations:
 low energy consumption  long watch time
 high goodput  more concurrent videos
 feasible schedule  no buffer over/underflow
2
Energy Saving for Mobile Receivers
3
Bit Rate
Burst
Overhead To
R
Off
r
 This is called Time Slicing (in broadcast standards)
 Need to construct feasible burst schedules
Time
- no two bursts overlap with each other in time
- no receiver buffer over/underflow instances
 How do commercial base stations construct schedules?
Commercial Base Stations



Such as UDCast IPE-10 and UBS DVE-6000
Schedule bursts in Round-Robin fashion
 operators manually choose a scheduling window
size T and burst sizes bs for all stream s
Transmit a video stream in its assigned time slot
Bit Rate

R
 
b1 b2 b3

T
Slotted Scheduling
Time
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Slotted Scheduling


Fine, if all video streams are encoded at the same
and constant bit rate
But most videos are VBR and at different bit rates
 choosing parameters manually is error-prone
 RR is not efficient in terms of energy saving
Overhead To
R
T
Time
A more flexible scheduler is needed!
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Problem Statement

Given multiple VBR streams, construct a feasible
burst schedule for them to maximize energy saving
and goodput
 no burst overlaps, no buffer over/underflow
instances
 bursts are flexible in sizes and start times
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Hardness



Theorem: burst scheduling to maximize energy saving
is NP-Complete [ToN'09]
Proof Sketch:
 We show that maximizing energy saving is the same
as minimizing number of bursts
 Then, we reduce the task sequencing problem with
release times and deadlines problem to it
Corollary: scheduling to maximize goodput and energy
saving is NP-Complete
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Problem Formulation
Goodput: amount of on-time delivered data over
broadcast network capacity
Energy saving: fraction of time mobile receivers
can turn off their receiving circuits
No two bursts overlap with each other
No buffer underflow instances
No buffer overflow instances
The problem is NP-complete  approx algorithm
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Observation
Hardness is due to tightly-coupled constraints: no
burst overlap & no buffer under/overflow instances
machine scheduling algorithms may lead to playout
glitches
Buffer Fullness
Buffer Fullness

Time
Buffer Fullness

Time
Buffer Underflow
Time
Buffer Overflow
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Decouple These Two Constraints




Transform the formulation into a new one, in
which any feasible schedule leads to no buffer
violation instances in the original formulation
Solve the transformed formulation efficiently
 only one constraint: no overlaps
Convert the schedule for the original formulation
Ensure correctness and bound optimality gap in
all steps
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Transform


Transform idea
 divide receiver buffer into two: B and B’
 divide the broadcast time into multiple windows
 in each window, drain data from B while filling
B’ and vice versa
Goal of transformed formulation
 schedule bursts, so that bits consumed in the
current window = bits received in the preceding
window
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Buffer Dynamics
Buf B
Fill
Buf B’
Fullness
Window 1
Drain
Window 2
Drain
Fill
Window 3
Fill
Drain
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Construct Windows




Windows are constructed using video traces
yp: required amount of received data in window p
 compute the maximum number of frames can be
included in B or B’  for higher energy saving
xp: start time of window p
zp: end time of window p
 follow frame rate (fps) and number of frames
sent in immediately previous window
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Transformed Formulation
 No buffer
under/overflow
instances
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The SMS Algorithm
1. // transform
2. For each video stream, divide the broadcast time
into multiple windows based on the frame sizes
3. // note that each window is specified by
<required burst length, start time, end time>
4. // scheduling by decision points: (1) new window
starts, (2) window completes, and (3) window ends
5. For each decision point t, schedule a burst from
time t to tn for the window with the smallest end
time, where tn is the next decision point
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Analysis of the SMS Algorithm

Theorem [Correctness]: SMS gives feasible
burst schedules

Theorem [Optimality]: SMS returns optimal
schedules in terms of goodput

Theorem [Complexity]: SMS runs in time O(PS
+ S2), where S is the number of TV channels
and P is the maximum number of windows
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
Analysis on the SMS Algorithm (cont.)


Theorem [Near-Optimality]: SMS returns nearoptimal schedules in terms of energy saving
The approximation gap is:
      T0 r /Q
*

, where r is the
average bit rate across all video streams,
and Q is receiver buffer size
How good is it?
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Numerical Analysis on Near-Optimality
energy saving achieved by SMS is at most 1.25% less
than the optimum, if average rate = 512 kbps, buffer
size > 1MB
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Simulation Setup




Implemented a simulator for DVB-H networks
 took H.264 VBR traces (from ASU) as input
Simulated 20 concurrent streams for 60 mins
Compared 3 algorithms: SMS, VBR, RVBR
 VBR: directly broadcast VBR streams
 RVBR: broadcast rate regulated VBR
Considered metrics: (i) fraction of missed
frames, (ii) maximum number of streams, (iii)
energy saving
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Simulation Results: Missed Frames
SMS results in almost no missed frame, while VBR
and RVBR have up to 33% and 12% missed frames
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Simulation Results: Number of Streams
SMS allows broadcasting 20 streams, while RVBR and
VBR allow 14 and less than 3
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Simulation Results: Energy Saving
SMS achieves energy saving 2—7% lower than a
conservative upper bound (UB), and is better than
VBR and RVBR for up to 12% and 5%
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Implementation on Mobile TV Testbed




Implemented SMS in our mobile TV testbed
 a Linux base station, protocol analyzer, and
smart phones as receivers [MM’08Demo]
Encoded five videos (from CBC) into mp4 files
with H.264 video and MPEG-4 AAC audio
Concurrently broadcast 20 mp4 files
Measured burst times to validate correctness
and compute per-channel energy saving
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Experimental Results: Energy Saving
SMS achieves high energy saving in real testbed: about
80% for 768 kbps streams and 93% for 250 kbps ones
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Conclusion




An efficient burst scheduling algorithm for
transmitting VBR streams in broadcast networks
The algorithm is optimal on goodput and nearoptimal on energy saving
Achieve high energy saving: at most 1.25% worse
than optimum
Evaluated the algorithm with simulations and a
real mobile TV testbed
 good streaming quality, high goodput, and high
energy saving
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Thank You, and Questions?
More details can be found online at http://nsl.cs.sfu.ca
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Related Work


Joint rate control among TV channels [TMM’08]
 assume joint encoders/transcoders are collocated
with base station  expensive
 fixed burst schedules  still RR
Statistical multiplexing without look-ahead
windows [IJDMB’09]
 predict the short-term VBR traffic pattern 
nondeterministic next burst time
 flexible burst lengths  still RR
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