Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE

Adaptive Delivery of
Live Video Streams
Infrastructure Cost vs. QoE
Gwendal Simon

Context and
Motivations
2 / 30 Gwendal Simon Adaptive Delivery of Live Video Streams

Context
Target applications : live streaming platforms

Context
Target applications : live streaming platforms
Target network : CDN-based delivery architecture
Content Provider
encoders
ingest
server
CDN
origin
server
edge
servers
Clients

Problem
one ingested stream = a dozen of representations

Problem
= tens of Mbps to deliver
to each edge server

Problem
= tens of Mbps to deliver
to each edge server
thousands of streams = a huge stress on the
CDN infrastructure

Motivations
Our goal : ﬁnd a better trade-oﬀ between
The Quality of Experience (QoE) at the user side
The CDN infrastructure cost

Motivations
Our goal : ﬁnd a better trade-oﬀ between
The Quality of Experience (QoE) at the user side
The CDN infrastructure cost
Our approach : optimization, bounds, heuristics

Contributions
Content Provider
encoders
ingest
server
CDN
origin
server
edge
servers
Clients

Contributions
Content Provider
encoders
ingest
server
CDN
origin
server
edge
servers
Clients
Contribution 1
Optimizing transcoding in the ingest server
ingest
server
origin
server

Contributions
Content Provider
encoders
ingest
server
CDN
origin
server
edge
servers
Clients
Contribution 2
Optimizing the delivery in under-provisioned network
CDN
origin
server
edge
servers
Contribution 1
Optimizing transcoding in the ingest server

Case study : Twitch

What is a broadcaster
Anybody who screencasts, encodes, and uploads
online online
nb. of viewers
time
t1 t1 t2 t2
session 1 session 2

Data retrieval
Three months in 2014 : from Jan. 6th to Mar. 6th
time
all
channels
of one
snapshot
9 :00 9 :05 9 :10 9 :15 9 :20
every ﬁve minutes → one snapshot
Dataset available : http ://dash.ipv6.enstb.fr/dataset/twitch/

How many online broadcasters
0 10 20 30 40 50 60 70 80 90
0
2K
4K
6K
8K
10K
Days
Nb.ofonlinechannels
min max

How much bandwidth is needed
0 10 20 30 40 50 60 70 80 90
0
1
2
Days
Bandwidth(Tbps)
min max

Characteristics of the ingested videos
240p 360p 480p 720p 1080p
0
0.2
0.4
0.6
Representation
Sessionsratio
0.1 1 10
0
0.25
0.5
0.75
1
Video bit-rate (Mbps)
CDFofthesessions
720p

Characteristics of the ingested videos
240p 360p 480p 720p 1080p
0
0.2
0.4
0.6
Representation
Sessionsratio
0.1 1 10
0
0.25
0.5
0.75
1
Video bit-rate (Mbps)
CDFofthesessions
720p 480p 1080p

Optimizing
Transcoding

Implementing adaptive streaming
ingest
server
delivery
network
broadcaster

ingest
server
delivery
network
broadcaster
How many representations ?
What bit-rates ?
What resolutions ?

ingest
server
delivery
network
broadcaster
broadcaster
Popular user-generated platforms
need transcoding-as-a-service oﬀers

ingest
server
delivery
network
broadcaster
broadcaster
broadcaster
Transcode according to :
stream type
stream popularity
stream resolution and bit-rate

A dataset for transcoding ingested videos
original
video
yuv
broadcaster-
prepared video
1080p
2.75 Mbps
encoding

original
video
yuv
broadcaster-
prepared video
1080p
2.75 Mbps
encoding
cloud-
transcoded repr.
360p
1.6 Mbps
transcoding
measure CPU cycles
500 1000 1500 2000 2500
0.6
0.8
1
1.2
1.4
1.6
Rate (in kbps)
CPU(inGHz)
224p 360p 720p 1080p

original
video
yuv
broadcaster-
prepared video
1080p
2.75 Mbps
encoding
cloud-
transcoded repr.
360p
1.6 Mbps
transcoding
measure CPU cycles
reference video
360p
3 Mbps
estimating
QoE
500 1000 1500 2000 2500
30
32
34
36
38
40
Rate (in kbps)
PSNR(indB)
224p 360p 720p 1080p
500 1000 1500 2000 2500
0.6
0.8
1
1.2
1.4
1.6
Rate (in kbps)
CPU(inGHz)
224p 360p 720p 1080p

Problem Formulation
a type of video
a resolution
a bit-rate
Ingested streams

Problem Formulation
a type of video
a resolution
a bit-rate
Ingested streams
a max download capacity
a max display size
a stream to watch
End-users

Problem Formulation
a type of video
a resolution
a bit-rate
Ingested streams
a max display size
a stream to watch
End-users
limited CPU resources
limited delivery capacity
Constraints

Problem Formulation
a type of video
a resolution
a bit-rate
Ingested streams
a max display size
a stream to watch
End-users
limited CPU resources
limited delivery capacity
Constraints
decide for each stream :
nb of representations
their resolutions
their bit-rates

Integer Linear Program (ILP)
max
{ααα,βββ}
i∈I o∈O u∈U
fiou · αiou (1a)
s.t. i ∈ I, o ∈ O, u ∈ U (1b)
o∈O
αiou ≤ diu, i ∈ I, u ∈ U (1c)
i∈I o∈O
ro − cu · αiou ≤ 0, u ∈ U (1d)
i∈I o∈O u∈U
αiou ≥ R · N, (1e)
βiom ≤
1, if (vi = vo & si = so & bi > bo)
(vi = vo & si > so & bi ≥ bo)
0, otherwise
i ∈ I, o ∈ O, m ∈ M (1f)
i∈I o∈O
pio · βiom ≤ Pm, m ∈ M (1g)
... (1h)

How far from the optimal ?
1. Deﬁne simulation settings using three datasets :
A collection of broadcasters’ streams
A population of end-viewers
A transcoding dataset of QoE and CPU measures

2. Compute optimal representations with ILP

2. Compute optimal representations with ILP
3. Compare ILP optimum with current solutions :
Full-cover strategies : the smallest rate per resolution
Cloud-transcoding providers sets (e.g. Zencoder)

The 50 most popular channels are transcoded
20 40 60 80 100
29
30
31
32
33
number of machines
Avg.PSNR(indB)

20 40 60 80 100
29
30
31
32
33
Full-Cover
number of machines
Avg.PSNR(indB)

20 40 60 80 100
29
30
31
32
33
Full-Cover Zencoder
number of machines
Avg.PSNR(indB)

Heuristic in a nutshell
Input : streams + total CPU
Process each stream

Process each stream
Fix a stream CPU based on optimum :
1) stream popularity
2) stream CPU < 10GHz
3) video type
4) resolution

Process each stream
3) video type
4) resolution
Find stream representations w.r.t CPU

Process each stream
3) video type
4) resolution
Find stream representations w.r.t CPU
Any pending stream ? Next stream
Output : Representations
yes
no

Algorithms performance
0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 20 000
31
31.5
32
32.5
33
Total CPU (GHz)
Avg.PSNR(dB)
Full-Cover

0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 20 000
31
31.5
32
32.5
33
Total CPU (GHz)
Avg.PSNR(dB)
Full-Cover Zencoder

0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 20 000
31
31.5
32
32.5
33
Total CPU (GHz)
Avg.PSNR(dB)
Full-Cover Zencoder Heuristic

More details in
L. Toni, R. Aparicio-Pardo, K. Pires, A. Blanc, G. Simon and P. Frossard.
Optimal Selection of Adaptive Streaming Representations
ACM Transactions on Multimedia Computing, Communications and Applications, 2015.
R. Aparicio, K. Pires, A. Blanc and G. Simon.
Transcoding Live Video Streams at a Massive Scale in the Cloud
in Proc. of ACM MMSys, 2015.

Optimizing Delivery

Model
ISP 1 ISP 2 ISP 3
origin servers
reﬂectors
edge servers

Model
ISP 1 ISP 2 ISP 3
origin servers
reﬂectors
edge servers
Assumption : The upload capacity of the
equipments is the main resource to save

Our idea in a nutshell
Do not send all representations to edge servers

CDN
coordinator

CDN
coordinator
1
edge servers
report to the
CDN coordinator
about the
requests they
got during the
last period
1

CDN
coordinator
2
The CDN coor-
dinator decides
utility scores
for all repre-
sentations and
all edge servers
2

CDN
coordinator
3
sources deliver
representations so
that the overall
utility score is
maximal, subject
to network
under-provisioning
3

A simple example of delivery
4
6
4
5
mobile ftth other mobile
reﬂectors with upload
capacity expressed in Mbps

4
6
4
5
mobile ftth other mobile
One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps

One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps
4
6
4
5
mobile
ulow = 6
uhigh = 1
ftth
ulow = 3
uhigh = 9
other
ulow = 4
uhigh = 4
mobile
ulow = 6
uhigh = 1

One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps
2
6
1
4
mobile
ulow = 6
uhigh = 1
ftth
ulow = 3
uhigh = 9
other
ulow = 4
uhigh = 4
mobile
ulow = 6
uhigh = 1

One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps
2
6
1
4
mobile
ulow = 6
uhigh = 1
ftth
ulow = 3
uhigh = 9
mobile
ulow = 6
uhigh = 1
other
ulow = 4
uhigh = 4

One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps
2
3
1
1
mobile
ulow = 6
uhigh = 1
ftth
ulow = 3
uhigh = 9
mobile
ulow = 6
uhigh = 1
other
ulow = 4
uhigh = 4
e ulow + uhigh = 23

One stream
Two representations
low-quality 1 Mbps
high-quality 3 Mbps
2
3
1
1
mobile
ulow = 6
uhigh = 1
ftth
ulow = 3
uhigh = 9
mobile
ulow = 6
uhigh = 1
other
ulow = 4
uhigh = 4
e ulow + uhigh = 23
Can we do better ?

What we have done
J. Liu, G. Simon, G. Texier, and C. Rosenberg.
User-centric discretized delivery of rate-adaptive live
streams in underprovisioned CDN networks
IEEE Journal in Selected Areas in Communications, 2014.

What we have done
A linear program to compute the optimal delivery
Only on small-scale network and few movies

What we have done
Some fast optimal algorithms for special cases

What we have done
Some fast optimal algorithms for special cases
A fast heuristic with near-optimal performances

A glimpse of simulation results
provisioning only 1
5 of a full delivery
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
User satisfaction
CDFofusers

provisioning only 1
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
User satisfaction
CDFofusers
A naive approach

provisioning only 1
two thirds of users do
not get the best repr.
a ﬁfth of users
experience quality
half the optimal
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
User satisfaction
CDFofusers
A naive approach

provisioning only 1
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
User satisfaction
CDFofusers
A naive approach
Our optimal solution

provisioning only 1
room for improvement
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
User satisfaction
CDFofusers
A naive approach
Our optimal solution

Conclusion

Takeaway
Adaptive streaming can be harmful for infrastructures

Takeaway
Current implementations are far from optimal

Takeaway
Smarter solutions exist

Takeaway
Smarter solutions exist
Signiﬁcant improvements can be obtained

Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE

Similar to Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE (20)

More from Gwendal Simon

More from Gwendal Simon (12)

Recently uploaded

Recently uploaded (20)

Adaptive Delivery of Live Video Stream: Infrastructure cost vs. QoE