adaptive delivery of live video stream: infrastructure cost vs. qoe

Adaptive Delivery ofLive Video StreamsInfrastructure Cost vs. QoEGwendal Simon

Context andMotivations

2 / 30 Gwendal Simon Adaptive Delivery of Live Video Streams

Context

Target applications : live streaming platforms

Target network : CDN-based delivery architecture

Content Provider

encodersingestserver

originserver

edgeservers Clients

Context

Target applications : live streaming platforms

Target network : CDN-based delivery architecture

Content Provider

originserver

edgeservers Clients

Problem

one ingested stream = a dozen of representations

= tens of Mbps to deliverto each edge server

thousands of streams = a huge stress on theCDN infrastructure

Problem

one ingested stream = a dozen of representations= tens of Mbps to deliver

to each edge server

Problem

one ingested stream = a dozen of representations= tens of Mbps to deliver

to each edge server

Motivations

Our goal : find a better trade-off betweenThe Quality of Experience (QoE) at the user sideThe CDN infrastructure cost

Our approach : optimization, bounds, heuristics

Motivations

Our goal : find a better trade-off betweenThe Quality of Experience (QoE) at the user sideThe CDN infrastructure cost

Our approach : optimization, bounds, heuristics

Contributions

Content Provider

originserver

edgeservers Clients

Contribution 2Optimizing the delivery in under-provisioned network

originserver

edgeservers

Contribution 1Optimizing transcoding in the ingest server

ingestserver

originserver

Contributions

Content Provider

originserver

edgeservers Clients

originserver

edgeservers

ingestserver

originserver

Contributions

Content Provider

originserver

edgeservers Clients

originserver

edgeservers

ingestserver

originserver

Case study : Twitch

What is a broadcaster

Anybody who screencasts, encodes, and uploads

online online

nb. of viewers

timet1 t ′

1 t2 t ′2

session 1 session 2

Data retrieval

Three months in 2014 : from Jan. 6th to Mar. 6th

allchannelsof one

snapshot

9 :00 9 :05 9 :10 9 :15 9 :20every 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 80 900

linechannels

min max

How much bandwidth is needed

0 10 20 30 40 50 60 70 80 900

s)min max

Characteristics of the ingested videos

240p 360p 480p 720p 1080p0

Representation

Sessions

0.1 1 100

Video bit-rate (Mbps)CD

thesessions

Characteristics of the ingested videos

240p 360p 480p 720p 1080p0

Representation

Sessions

0.1 1 100

Video bit-rate (Mbps)CD

thesessions

720p 480p 1080p

OptimizingTranscoding

Implementing adaptive streaming

ingestserver

deliverynetworkbroadcaster

How many representations ?What bit-rates ?What resolutions ?

broadcaster

Popular user-generated platformsneed transcoding-as-a-service offers

broadcaster

broadcasterbroadcaster

Transcode according to :stream type

stream popularitystream resolution and bit-rate

ingestserver

deliverynetworkbroadcaster

broadcaster

ingestserver

deliverynetwork

broadcaster

ingestserver

deliverynetwork

broadcaster

A dataset for transcoding ingested videos

originalvideoyuv

broadcaster-prepared video

1080p2.75 Mbps

encoding

cloud-transcoded repr.

360p1.6 Mbps

transcoding

measure CPU cycles

reference video360p

3 Mbps

estimatin

500 1000 1500 2000 2500303234363840

Rate (in kbps)

PSNR(in

224p 360p 720p 1080p

500 1000 1500 2000 25000.60.81

1.21.41.6

Rate (in kbps)

(inGHz)

224p 360p 720p 1080p

originalvideoyuv

1080p2.75 Mbps

encoding cloud-transcoded repr.

360p1.6 Mbps

transcoding

measure CPU cycles

reference video360p

3 Mbps

estimatin

500 1000 1500 2000 2500303234363840

Rate (in kbps)

PSNR(in

224p 360p 720p 1080p

500 1000 1500 2000 25000.60.81

1.21.41.6

Rate (in kbps)

(inGHz)

224p 360p 720p 1080p

originalvideoyuv

1080p2.75 Mbps

encoding cloud-transcoded repr.

360p1.6 Mbps

transcoding

measure CPU cycles

reference video360p

3 Mbps

estimatin

500 1000 1500 2000 2500303234363840

Rate (in kbps)

PSNR(in

224p 360p 720p 1080p

500 1000 1500 2000 25000.60.81

1.21.41.6

Rate (in kbps)

(inGHz)

224p 360p 720p 1080p

Problem Formulation

a type of videoa resolutiona bit-rate

Ingested streams

a max download capacitya max display sizea stream to watch

End-users

limited CPU resourceslimited delivery capacity

Constraints

decide for each stream :nb of representations

their resolutionstheir bit-rates

Problem Formulation

Ingested streams

End-users

Constraints

Problem Formulation

Ingested streams

End-users

Constraints

Problem Formulation

Ingested streams

End-users

Constraints

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’ streamsA population of end-viewersA transcoding dataset of QoE and CPU measures

2. Compute optimal representations with ILP

3. Compare ILP optimum with current solutions :Full-cover strategies : the smallest rate per resolutionCloud-transcoding providers sets (e.g. Zencoder)

The 50 most popular channels are transcoded

20 40 60 80 100

number of machines

20 40 60 80 100

Full-Cover

number of machines

20 40 60 80 100

Full-Cover Zencoder

number of machines

Heuristic in a nutshellInput : streams + total CPU

Process each stream

Fix a stream CPU based on optimum :1) stream popularity2) stream CPU < 10GHz3) video type4) resolution

Process each stream

Find stream representations w.r.t CPU

Process each stream

Find stream representations w.r.t CPU

Any pending stream ? Next stream

Output : Representations

Algorithms performance

0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 20 00031

Total CPU (GHz)

PSNR(dB)

Full-Cover

0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 20 00031

Total CPU (GHz)

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 00031

Total CPU (GHz)

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

ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers

Assumption : The upload capacity of theequipments is the main resource to save

ISP 1 ISP 2 ISP 3

origin servers

reflectors

edge servers

Assumption : The upload capacity of theequipments is the main resource to save

Our idea in a nutshell

Do not send all representations to edge servers

CDNcoordinator

edge serversreport to the

CDN coordinatorabout the

requests theygot during thelast period

The CDN coor-dinator decidesutility scoresfor all repre-sentations andall edge servers

23 sources deliverrepresentations sothat the overallutility score is

maximal, subjectto network

under-provisioning

CDNcoordinator

under-provisioning

CDNcoordinator

2The CDN coor-dinator decidesutility scoresfor all repre-sentations andall edge servers

under-provisioning

CDNcoordinator

2The CDN coor-dinator decidesutility scoresfor all repre-sentations andall edge servers

under-provisioning

CDNcoordinator

under-provisioning

A simple example of delivery

mobile ftth other mobile

reflectors with uploadcapacity expressed in Mbps

One streamTwo representations

low-quality 1 Mbpshigh-quality 3 Mbps

mobileulow = 6uhigh = 1

ftthulow = 3uhigh = 9

otherulow = 4uhigh = 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 livestreams in underprovisioned CDN networksIEEE Journal in Selected Areas in Communications, 2014.

A linear program to compute the optimal deliveryOnly on small-scale network and few movies

Some fast optimal algorithms for special casesA fast heuristic with near-optimal performances

What we have done

Some fast optimal algorithms for special cases

A fast heuristic with near-optimal performances

What we have done

A glimpse of simulation results

provisioning only 15 of a full delivery

0 0.2 0.4 0.6 0.8 10

User satisfaction

0 0.2 0.4 0.6 0.8 10

User satisfaction

A naive approach

two thirds of users donot get the best repr.

a fifth of usersexperience qualityhalf the optimal

0 0.2 0.4 0.6 0.8 10

User satisfaction

A naive approach

0 0.2 0.4 0.6 0.8 10

User satisfaction

A naive approachOur optimal solution

room for improvement

0 0.2 0.4 0.6 0.8 10

User satisfaction

A naive approachOur optimal solution

Conclusion

Takeaway

Adaptive streaming can be harmful for infrastructures

Current implementations are far from optimal

Smarter solutions exist

Significant improvements can be obtained

Takeaway

adaptive delivery of live video stream: infrastructure cost vs. qoe

Technology

thecdn infrastructure4

huge stress

dozen of representations

tens of mbps

viewerstimet1 t

snapshotdataset available

uploadsonline onlinenb