Considering network conditions, video content, and viewer device type/screen resolution to construct a bitrate ladder is necessary to deliver the best Quality of Experience (QoE). A large-screen device like a TV needs a high bitrate with high resolution to provide good visual quality, whereas a small one like a phone requires a low bitrate with low resolution. In addition, encoding high-quality levels at the server side while the network is unable to deliver them causes unnecessary cost for the content provider. Recently, the Common Media Client Data (CMCD) standard has been proposed, which defines the data that is collected at the client and sent to the server with its HTTP requests. This data is useful in log analysis, quality of service/experience monitoring and delivery improvements. In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages CMCD to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.

1. All rights reserved. ©2020
All rights reserved. ©2020
CADLAD: Device-aware Bitrate Ladder Construction
for HTTP Adaptive Streaming
CNSM 2022 | Thessaloniki, Greece | 31 October - 4 November 2022
Minh Nguyen, Babak Taraghi, Abdelhak Bentaleb, Roger Zimmermann, Christian Timmerer
Alpen-Adria Universität Klagenfurt, Austria
National University of Singapore, Singapore
minh.nguyen@aau.at | athena.itec.aau.at
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2. All rights reserved. ©2020
● Introduction
● Motivation
● Proposed Approach - CADLAD
● Evaluation
● Conclusions
Agenda
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Introduction
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Video Is Everywhere
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Heterogeneous devices for
watching video content [2]
[2] Bitmovin, “Video Developer Report 2021,” [Online] Available: https://go.bitmovin.com/video-developer-report-2022
[1] Sandvine, “Global internet phenomena report 2022, https://www.sandvine.com/phenomena
54%
Video streaming in
overall traffic [1]

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HTTP Adaptive Streaming
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Server
...
HTTP GET requests
Video
segmentation
Video encoding
...
...
...
Version 3
Version 2
Version 1
Client
Adaptive bitrate
algorithm
Throughput
estimation
Playout buffer
Video decoding
Throughput
Time

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Media Presentation Description (MPD) File
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● Quality version 1: bitrate 1, height 1, width 1
● Quality version 2: bitrate 2, height 2, width 2
● Quality version 3: bitrate 3, height 3, width 3
● …
MPD file holding information of quality versions is sent from
the server to the client
Adaptive bitrate
algorithm
Bitrate X

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Buffer
length
Measured
throughput
Top
bitrate …
bl
mtp tb
Metrics defined in CMCD
Common Media Client Data (CMCD)
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Server Client
…. sw
dt
Screen
width
Device
type
Metrics proposed
CMCD specification: https://cdn.cta.tech/cta/media/media/resources/standards/pdfs/cta-5004-final.pdf
How to use CMCD? How to calculate CMCD?
Bitrate
ladder

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Proposed Approach - CADLAD
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CMCD Parameter Determination
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Screen
width
720p 1080p 2160p
[1]
[1] https://netflixtechblog.com/vmaf-the-journey-continues-44b51ee9ed12
Device
type
mobile desktop TV
Top
bitrate
Average
throughput

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1. VoD Scenario
b2, w2
b1, w1
b3, w3
b4 <= tb3, w4 <= sw3
b2 <= tb1, w2 <= sw1
b1, w1
b2, w2
b1, w1
b3 <= tb2, w3 <= sw2
Bitrate Ladder Construction
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Server
b4, w4
b3, w3
b2, w2
b1, w1
Q
u
a
l
i
t
y
v
e
r
s
i
o
n
s
(tb3, tv, sw3)
(tb1, mobile, sw1)
(tb2, desktop, sw2)
(Top bitrate, Device type, Screen width)
MPD 3
MPD 2
MPD 1

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2. Live scenario
2.1 Encoding
Bitrate Ladder Construction
(1) Collection
(2) Classification
(3) K-means clustering
(4) Bitrate ladder
selection
(5) Encoding
…
…
…
…
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2. Live scenario
2.1 Encoding
b2, w2
b1, w1
b3, w3
b4 <= tb3, w4 <= sw3
b2 <= tb1, w2 <= sw1
b1, w1
b2, w2
b1, w1
b3 <= tb2, w3 <= sw2
Bitrate Ladder Construction
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MPD 3
MPD 2
MPD 1
Server
…

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Evaluation
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Experimental setup
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○ CAdViSE: Adaptive Streaming Players Performance Testbed [1]
○ Bitrate ladder: {100, 200, 375, 550, 750, 1000, 1500, 3000, 5800, 7500, 12000, 17000}
with resolution from 144p to 2160p. Video: Seconds that count [2]
○ Network:
■ 4G LTE trace [3] - 1 client
■ Cascade trace - Multiple clients
{200, 100, 50, 25, 50, 100, 200}Mbps
○ CADLAD is implemented in dashjs v4 player
■ CADLAD-T: TV devices
■ CADLAD-D: desktop devices
■ CADLAD-M: mobile devices
■ CADLAD-A: all types of devices
[1] B. Taraghi, et.al., “CAdViSE: cloud-based adaptive video streaming evaluation framework for the automated testing of media players,” in Proceedings of the 11th
ACM Multimedia Systems Conference, 2020, pp. 349–352.
[2] Taraghi, B., et. al.. “Multi-codec ultra high definition 8K MPEG-DASH dataset”. In Proceedings of the 13th ACM Multimedia Systems Conference(pp. 216-220).
[3] D. Raca, J. J. Quinlan, A. H. Zahran, and C. J. Sreenan, “Beyond throughput: a 4G LTE dataset with channel and context metrics,” in Proceedings of the 9th ACM
Multimedia Systems Conference, 2018, pp. 460 - 465
Server
Clients
Controlled
Network

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Evaluation Metrics
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Bitrate The average bitrate of all segments downloaded by
same-device end users in a streaming session.
# of switches The average number of quality switches of same-device
end users in a streaming session.
Stall duration The average period while the video is frozen at
same-device end users.
QoE score The QoE score calculated by model ITU-T P.1203 mode 1

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Experimental results
1. VoD streaming
● CADLAD outperforms dashjs v4 (dashjs4)
● Stall duration by 64-100%
● # of switches by 12-90%
● Save data usage with lower average bitrate
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Evaluation Metrics
1. VoD streaming
QoE by up to 2.7x
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Experimental results
2. Live streaming
● CADLAD outperforms dashjs v4 (dashjs4)
● Stall duration by at least 20%
● # of switches in most cases
● Save data usage with lower average bitrate
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Evaluation Metrics
2. Live streaming
QoE by up to 2.5x
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Conclusions
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Conclusions
● Proposing a CMCD-aware per-device bitrate ladder construction,
namely CADLAD
● Providing the server:
○ the top bitrate (tb)
○ the device type (dt)
○ the screen width (sw)
● Experiential results
○ Significantly improving the QoE
○ Saving substantial downloaded data to the clients
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22. Thank you
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minh.nguyen@aau.at https://twitter.com/minhkstn https://www.linkedin.com/in/minhkstn/