The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices. In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.

1. Exploring the Energy Consumption of Video
Streaming: Components, Challenges,
and Opportunities
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 estimation considerations
Components of energy consumption
Challenges & opportunities
Publications
Remarks

4. 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

5. 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

6. 5
GAIA Project Leaders

7. 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

8. Carbon
Estimation
Considerations

9. Carbon Estimation Considerations
Energy sources
Imported electricity
The role of timely electricity usage
8

10. Components
of Energy
Consumption

11. 10
Components of Energy Consumption in
Video Streaming
Encoding
Storage
Core network
Access networks
Edge components
NIC
Decode
Display
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/

12. Opportunities
Challenges &

13. 12
Energy Consumption in Video Encoding
Energy-efficient codecs
Maximizing efficiency through
parameter optimization
Efficient transcoding techniques
Compression algorithm complexity

14. 13
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

15. 14
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

16. 15
Networks
Exploring end-users' perception of quality
to reduce video streaming bitrates and
energy consumption
Automatic energy-efficient path selection
for video streaming

17. 16
Hybrid CDNs
Multi-CDN
Maximizing CDN efficiency
Hit-ratio metric is not enough
On-demand segment caching
CDN

18. 17
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

19. 18
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)

20. 19
Optimizing energy consumption in interconnected video streaming
components
Tracked metrics for energy logging
End-to-end Perspective

21. Publications

22. 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/
21

23. 22
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/

24. 23
ECO Player

25. Optimizing Video Streaming for
Sustainability and Quality: The Role of
Preset Selection in Per-Title Encoding
24
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/

26. Remarks
25
Challenges and opportunities to reduce video streaming energy consumption
Several key aspects should be highlighted when designing an energy-efficient
video streaming approach
Considering crucial components for accurately assessing energy usage
A holistic system design approach
Raising awareness of the environmental impact of video streaming among
users
publications
Green Video Streaming: Challenges and Opportunities
Video encoding matching-based model for Cloud and Edge computing
Instances
Eco player
The Role of preset selection in per-title encoding

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