This document discusses energy consumption in video streaming. It identifies the key components that consume energy, including encoding, storage, networks, edge components, decoding, and displays. Measurement tools and challenges are also covered. Strategies to reduce energy usage at each component are proposed, such as efficient encoding, optimized bitrates, CDN optimization, and energy-aware networking. A holistic, end-to-end approach is needed to minimize total energy consumption in video streaming.

1. Energy Consumption in Video Streaming:
Components, Measurements, and Strategies
Samira Afzal
Institute of Information Technology (ITEC), Alpen-Adria-Universität Austria
samira.afzal@aau.at | https://athena.itec.aau.at/

2. 1
Agenda
GAIA project
Carbon Emission Measurement
Components of Energy Consumption
in Video Streaming
Measurement Tools
Challenges & Strategies
Publications
Remarks

3. 2
ATHENA Team

5. Video streaming accounts for 67.60%
current network traffic
3
Motivation
https://www.bbc.com/future/article/20200305-why-your-internet-habits-are-not-as-clean-as-you-think
1
2
2
https://www.sandvine.com/hubfs/Sandvine_Redesign_2019/Downloads/2023/reports/Sandvine%20GIPR%202023.pdf
Urgent action is needed against climate change and global greenhouse gas
(GHG) emissions
The carbon footprint of Internet data traffic accounts for about 3.7% of
GHG
1

6. 4
GAIA (the Greek goddess of Earth, mother of all life, personification of the
Earth)
Funded by the Austrian Research Promotion Agency FFG (Basisprogramm)
A cooperative project between Bitmovin and Alpen-Adria-Universität
Klagenfurt (AAU)
GAIA: Intelligent Climate-Friendly Video
Platform

7. 5
GAIA Project Leaders

8. 6
Complete energy awareness and accountability, including energy
consumption and GHG emissions along the entire delivery chain, from
content creation and server-side encoding to video transmission and
client-side rendering
Reduced energy consumption and GHG emissions through advanced
analytics and optimizations on all phases of the video delivery chain
GAIA: Intelligent Climate-Friendly Video
Platform

9. Carbon
Emission
Measurement

10. 9
Variability and Uncertainty
Variability for carbon footprint measurements
Country-specific emission factor
End-user devices
Technological advances
Network energy intensity
Uncertainty about carbon footprint measurements
Differences in their allocation methods
Limited amount of data available publicly
Tools used for energy measurement
The Carbon Trust. The carbon impacts of video streaming. https: //www.carbontrust.com/our-work-and-
impact/guides-reports-and-tools/carbon-impact-of-video-streaming, 2021. Accessed: 2023-06-09

11. Carbon Estimation Considerations
Energy sources
Imported electricity
The role of timely electricity usage
10

12. 11
Methods to Measure Energy Consumption
Top-down approach
Bottom-up approach

13. Components
of Energy
Consumption

14. 13
Components of Energy Consumption in
Video Streaming
Encoding
Storage
Core network
Access networks
Edge components
NIC
Decode
Display

15. 14
Components of Energy Consumption in
Video Streaming
https://www.iea.org/reports/data-centres-and-data-transmission-networks
1
Data Centers & Networks accounted for approximately
300 Mt CO2-eq in 2020, equivalent to 0.9 % of energy-
related GHG emissions (or 0.6 % of total GHG emissions),
corresponding to 2-3% of global electricity usage
1
End-user devices 72 %,
data transmission 23 %
data centers 5 % 1

16. Monitoring instrumentation also demands
energy
Riekstin, Ana Carolina, et al. "A survey on
metrics and measurement tools for
sustainable distributed cloud networks."
IEEE Communications Surveys & Tutorials
20.2 (2017): 1244-1270.
Khan, Muhammad Umair, et al. "Measuring
power consumption in mobile devices for
energy sustainable app development: A
comparative study and challenges."
Sustainable Computing: Informatics and
Systems 31 (2021): 100589.
Measurement Tools
15

17. Strategies
Challenges &

18. 17
Energy Consumption in Video Encoding
Energy-efficient codecs
Maximizing efficiency through
parameter optimization
Efficient transcoding techniques
Compression algorithm complexity

19. 18
Energy Consumption in Datacenters
On-demand segment encoding
Optimizing video bitrate ladder
Eliminating perceptually redundant
representations
Per-title optimizing bitrate ladders
Encoding and storing segments in ABR

20. 19
Energy Consumption in Datacenters
Energy-efficient video encoding task
scheduler on the computing continuum
Suitable cloud services
Low-carbon cloud regions
Energy-based pricing options for cloud
computing platform providers
Power overhead in Cloud
instances

21. 20
Networks
Exploring end-users' perception of quality
to reduce video streaming bitrates and
energy consumption
Automatic energy-efficient path selection
for video streaming

22. 21
Hybrid CDNs
Multi-CDN
Maximizing CDN efficiency
Hit-ratio metric is not enough
On-demand segment caching
CDN

23. 22
Choosing energy-efficient NICs and
optimizing network settings
Use of more energy-efficient devices
Improving screen display technologies
Optimize video decoding algorithms
Optimizing video playback
Raising awareness of the environmental
impact of video streaming among users
End-User Devices

24. 23
Choosing energy-efficient NICs and
optimizing network settings
Use of more energy-efficient devices
Improving screen display technologies
Optimize video decoding algorithms
Optimizing video playback
Raising awareness of the environmental
impact of video streaming among users
End-User Devices
Subjective study on QoE ratings
QoE model of the high-quality user and the
green user [1]
[1] T. Hossfeld, M. Varela, L. Skorin-Kapov, P. E. Heegaard, A greener experience: Trade-offs between qoe and co2 emissions in today’s and 6g
networks, IEEE Communications Magazine (2023)

25. 24
Optimizing energy consumption in interconnected video streaming
components
Tracked metrics for energy logging
End-to-end Perspective

26. Publications

27. Green Video Streaming:
Challenges and Opportunities
Samira Afzal, Radu Prodan, and Christian Timmerer, Green Video Streaming: Challenges and Opportunities,
ACM SIGMM Records (2023)
https://athena.itec.aau.at/2023/04/ve-match-video-encoding-matching-based-model-for-cloud-and-edge-
computing-instances/
26

28. 27
VE-Match: Video Encoding
Matching-based Model for
Cloud and Edge Computing
Instances
Samira Afzal, Narges Mehran, Sandro Linder, Christian Timmerer, and Radu Prodan, VE-Match: Video
Encoding Matching-based Model for Cloud and Edge Computing Instances, ACM MMsys, GMSys workshop
(2023)
https://athena.itec.aau.at/2023/04/ve-match-video-encoding-matching-based-model-for-cloud-and-edge-
computing-instances/

29. 27
ECO Player

30. Optimizing Video Streaming for
Sustainability and Quality: The Role of
Preset Selection in Per-Title Encoding
28
Hadi Amirpour, Vignesh V Menon, Samira Afzal, Radu Prodan, Christian Timmerer, Optimizing Video
Streaming for Sustainability and Quality: The Role of Preset Selection in Per-Title Encoding, IEEE ICME (2023)
https://athena.itec.aau.at/2023/05/3483/

31. Remarks
29
Several key aspects should be highlighted when designing an energy-
efficient video streaming approach
Considering crucial components for accurately assessing energy usage
Strategies to reduce energy for each component
A holistic system design approach
Raising awareness of the environmental impact of video streaming among
users
Measurement approaches and tools

32. Thank you
Have a
great day
ahead!
Institute of Information Technology (ITEC) Alpen-Adria-Universität Austria
samira.afzal@aau.at https://itec.aau.at/