Network Assisted Rate Adaptation for Conversational Video ...conferences.sigcomm.org/sigcomm/2014/doc/slides/149.pdf · OVER LTE CSWS’14. Slide title 44 pt Text and bullet level

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CAPITALS

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{Ylva.Timner, Jonas.Pettersson, Hans.Hannu, Min.W.Wang, Ingemar.S.Johansson}@ericsson.com

NETWORK ASSISTED RATE ADAPTATION FOR CONVERSATIONAL VIDEO OVER LTE CSWS’14

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Network Assisted Rate Adaptation for Conversational Video over LTE, Concept and Performance Evaluation | ACM 978-1-4503-2991-0/14/08 | © Ericsson

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› Examples

– Google Congestion Control (GCC)

– Self-clocked rate adaptation (SCReAM)

› Bandwidth is estimated based on delay and/or packet loss

› Path capacity is probed

Endpoint based rate adaptation

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› Difficult to precisely determine congestion

–Over-reaction to handover

–Over-reaction to congestion

› Negative impact from other cross traffic

› Frequent (and large) bitrate changes

› Network service optimizations congestion may

become invisible to end users

› Fairness between users difficult to achieve

Endpoint based rate adaptation Issues

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network

assisted

Network assisted rate adaptation › Rate adaptation algorithm placed close to the air

interface bottle neck

› A session controller is in control of the bitrates

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Network assisted rate adaptation › Two algorithm alternatives examined:

–Bitrate fair

–Resource fair

› Network assisted rate values can be conveyed in

RTP header extensions (RFC5285) or in RTCP

application layer feedback messages (RFC4585)

framework can complement an endpoint based

solution

› RRC signaling can be used to forward the bitrates

to the session controller or directly to the

terminals.

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› All video users in a cell get the same rate

› A utilization target (e.g. 80%) controls the

resource split between video and other users

Bitrate fair

a c b

constant bitrate (bitrate fair)

high cost

low

cost

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›All users (regardless of service) should get

their fair share of the radio resources

Resource Fair (proportionally fair)

a c b

bitrate

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AB 2014 | Page 8

› The scheduler allocates transmission resources to

bearers (users)

› Scheduling interval: 1ms

› A scheduling weight controls the likelihood that a

bearer gets transmission resources

› Different scheduling algorithms

–Proportional fair

–Delay (optimized queuing delay)

LTE scheduling basics

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› FTP on “best effort” bearer:

– Proportional fair

› Conversational bearer:

– Delay scheduling

Bearer Configurations

Queuing delay

Schedulin

g w

eig

ht

Bitrate

Schedulin

g w

eig

ht

Proportional fair Delay

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AB 2014 | Page 11

Bitrate fair algorithm › Adaptation based on

channel utilization

measures:

–CUP :

video traffic +

tcp traffic.

–CUHP :

retransmissions +

video packets above

delay threshold

Done

Flowchart is executed

every 100ms or when

users enter or leave the

cell

Yes

Reduce rate quickly

Rnew = Rold (1 + (Maxhp / CUhp – 1))

NoCUhp > Maxhp

Send rate change

command to all video

users in cell

Change rate smootly

Rnew = Rold (1 + α (Maxp / CUp – 1))

|Rnew - Rold| / Rold > ThYes

No

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› >18 s : low CU, ramp up rate.

› 18-20s – good utilization

› Delays are rising at 21s

› Detected by High CUHP

›

Bitrate fair example

Recommended rate in cell

17 18 19 20 21 220

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Total utilization

High priority transmissions

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TX

bit

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Recommended

Rate in Cell

Used rates per

Video user

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Re

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Channel Utilization, CUP

High Prio CU, CUHP

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Bitrate fair example

Recommended rate in cell

17 18 19 20 21 220

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time [s]

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Total utilization

High priority transmissions

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1000

1200

time [s]

TX

bit

rate

[k

b/s

]

Recommended

Rate in Cell

Used rates per

Video user

17 18 19 20 21 220

0.05

0.1

0.15

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Channel Utilization, CUP

High Prio CU, CUHP

Low CU

Good CU – Keep Rate

Delays rising, High CUHP

– Increase Rate

– Decrease Rates fast

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› Determine target utilization 𝑢∗ (equal split):

› 𝑢∗ =1

𝑁𝑣𝑖𝑑𝑒𝑜+𝑁𝑜𝑡ℎ𝑒𝑟

› Use measured channel quality (potential rate) to determine

target rate, R, for each user i :

› 𝑢𝑖 =𝑅𝑖

𝑄𝑖 ⇒ 𝑅𝑖

∗ = 𝑢∗𝑄𝑖

› A closed loop part complements with compensation for

possible estimation errors

Resource fair algorithm

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› Rate changes

› Ability to keep fairness target

Resource fair example

6 8 10 12 14 16 18 20 22 240

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Co

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video users

TCP users

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Actual utilization

Wanted utilization

time [s]

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0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7

8

time [s]

active

use

rs

cell average video

cell 0 video

cell average ftp

cell 0 ftp

› 21 cells, 3GPP Case 1, 5 MHz

› Conversational video: 150 kbps – 1500 kbps

› Other traffic: Small file download 2 Mbit/s/cell

Simulation scenario

Example: Average 6 video users/cell

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› Resource fair achieves the highest average bitrate among

the network assisted rate adaptation algorithms

› …but is more conservative than endpoint solution at high

load

Bitrate

1 2 3 4 5 6 7 8 9 100

500

1000

1500

video users/cell

av

era

ge

co

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c r

ate

[k

bp

s]

Self clocked

rate fair

resource fair

fixed 150 kbps

fixed 300 kbps

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› Both network assisted algorithms manages to ensure low

latency even at high load levels

› Self-clocked algorithm works well only at lower load levels

Latency

1 2 3 4 5 6 7 8 9 100

50

100

150

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video users/cell

de

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tile

[m

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Self clocked

rate fair

resource fair

fixed 150 kbps

fixed 300 kbps

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AB 2014 | Page 21

› Flexible way to distribute system resources between

conversational video and other best effort traffic

– Fairness between users can be controlled

› The use of delay schedulers :

– Gives very low latency regardless of load

– Increased grace time upon congestion

› Precise discrimination between congestion and other non-

congestion related impairments

Conclusion

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AB 2014 | Page 22

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› Add a correction factor γ to the open loop:

› 𝑅𝑖∗ = 𝑢∗𝑄𝑖 1 + 𝛾

› Compute expected utilization:

› 𝑈𝑣𝑖𝑑𝑒𝑜∗ =

𝑁𝑣𝑖𝑑𝑒𝑜

𝑁𝑣𝑖𝑑𝑒𝑜+𝑁𝑜𝑡ℎ𝑒𝑟 if unlimited rates,

limit(𝑅𝑖∗)

𝑄𝑖(1+𝛾)

otherwise

› Adapt γ based on the relative error versus the measured

utilization Ûvideo :

› 𝑒 = 𝑈𝑣𝑖𝑑𝑒𝑜∗ − 𝑈 𝑣𝑖𝑑𝑒𝑜

› 𝛾𝑘 = 𝛾𝑘−1 + 𝐾𝑝 1 +Δ

𝑇𝑖𝑒𝑘 − 𝑒𝑘−1

Resource fair closed loop part

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Network Assisted Rate Adaptation for Conversational Video ...conferences.sigcomm.org/sigcomm/2014/doc/slides/149.pdf · OVER LTE CSWS’14. Slide title 44 pt Text and bullet level