What is the trade-off between CO2 emission and video-conferencing QoE?

Tobias Hoßfeld (University of Würzburg, Germany), Martin Varela (Profilence, Finland),  Lea Skorin-Kapov (University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia), Poul E. Heegaard (NTNU - Norwegian University of Science and Technology)  

It is a natural thing that users of multimedia services want to have the highest possible Quality of Experience (QoE), when using said services. This is especially so in contexts such as video-conferencing and video streaming services, which are nowadays a large part of many users’ daily life, be it work-related Zoom calls, or relaxing while watching Netflix. This has implications in terms of the energy consumed for the provision of those services (think of the cloud services involved, the networks, and the users’ own devices), and therefore it also has an impact on the resulting CO₂ emissions. In this column, we look at the potential trade-offs involved between varying levels of QoE (which for video services is strongly correlated with the bit rates used), and the resulting CO₂ emissions. We also look at other factors that should be taken into account when making decisions based on these calculations, in order to provide a more holistic view of the environmental impact of these types of services, and whether they do have a significant impact.

Energy Consumption and CO2 Emissions for Internet Service Delivery

Understanding the footprint of Internet service delivery is a challenging task. On one hand, the infrastructure and software components involved in the service delivery need to be known. For a very fine-grained model, this requires knowledge of all components along the entire service delivery chain: end-user devices, fixed or mobile access network, core network, data center and Internet service infrastructure. Furthermore, the footprint may need to consider the CO₂ emissions for producing and manufacturing the hardware components as well as the CO₂ emissions during runtime. Life cycle assessment is then necessary to obtain CO₂ emission per year for hardware production. However, one may argue that the infrastructure is already there and therefore the focus will be on the energy consumption and CO₂ emission during runtime and delivery of the services. This is also the approach we follow here to provide quantitative numbers of energy consumption and CO₂ emission for Internet-based video services. On the other hand, quantitative numbers are needed beyond the complexity of understanding and modelling the contributors to energy consumption and C02 emission.

To overcome this complexity, the literature typically considers key figures on the overall data traffic and service consumption times aggregated over users and services over a longer period of time, e.g., one year. In addition, the total energy consumption of mobile operators and data centres is considered. Together with the information on e.g., the number of base station sites, this gives some estimates, e.g., on the average power consumption per site or the average data traffic per base station site [Feh11]. As a result, we obtain measures such as energy per bit (Joule/bit) determining the energy efficiency of a network segment. In [Yan19], the annual energy consumption of Akamai is converted to power consumption and then divided by the maximum network traffic, which results again in the energy consumption per bit of Akamai’s data centers. Knowing the share of energy sources (nonrenewable energy, including coal, natural gas, oil, diesel, petroleum; renewable energy including solar, geothermal, wind energy, biomass, hydropower from flowing water), allows relating the energy consumption to the total CO₂ emissions. For example, the total contribution from renewables exceeded 40% in 2021 in Germany and Finland, Norway has about 60%, Croatia about 36% (statistics from 2020).

A detailed model of the total energy consumption of mobile network services and applications is provided in [Yan19]. Their model structure considers important factors from each network segment from cloud to core network, mobile network, and end-user devices. Furthermore, service-specific energy consumption are provided. They found that there are strong differences between the service type and the emerging data traffic pattern. However, key factors are the amount of data traffic and the duration of the services. They also consider different end-to-end network topologies (user-to-data center, user-to-user via data center, user-to-user and P2P communication). Their model of the total energy consumption is expressed as the sum of the energy consumption of the different segments:

  • Smartphone: service-specific energy depends among others on the CPU usage and the network usage e.g. 4G over the duration of use,
  • Base station and access network: data traffic and signalling traffic over the duration of use,
  • Wireline core network: service specific energy consumption of a mobile service taking into account the data traffic volume and the energy per bit,
  • Data center: energy per bit of the data center is multiplied by data traffic volume of the mobile service.

The Shift Project [TSP19] provides a similar model which is called the “1 Byte Model”. The computation of energy consumption is transparently provided in calculation sheets and discussed by the scientific community. As a result of the discussions [Kam20a,Kam20b], an updated model was released [TSP20] clarifying a simple bit/byte conversion issue. The suggested models in [TSP20, Kam20b] finally lead to comparable numbers in terms of energy consumption and CO₂ emission. As a side remark: Transparency and reproducibility are key for developing such complex models!

The basic idea of the 1 Byte Model for computing energy consumption is to take into account the time t of Internet service usage and the overall data volume v. The time of use is directly related to the energy consumption of the display of an end-user device, but also for allocating network resources. The data volume to transmit through the network, but also to generate or process data for cloud services, drives the energy consumption additionally. The model does not differentiate between Internet services, but they will result in different traffic volumes over the time of use. Then, for each segment i (device, network, cloud) a linear model E_i(t,v)=a_i * t + b_i * v + c_i is provided to quantify the energy consumption. To be more precise, the different coefficients are provided for each segment by [TSP20]. The overall energy consumption is then E_total = E_device + E_network + E_cloud.

CO₂ emission is then again a linear model of the total energy consumption (over the time of use of a service), which depends on the share of nonrenewable and renewable energies. Again, The Shift Project derives such coefficients for different countries and we finally obtain CO2 = k_country * E_total.

The Trade-off between QoE and CO2 Emissions

As a use case, we consider hosting a scientific conference online through video-conferencing services. Assume there are 200 conference participants attending the video-conferencing session. The conference lasts for one week, with 6 hours of online program per day.  The video conference software requires the following data rates for streaming the sessions (video including audio and screen sharing):

  • high-quality video: 1.0 Mbps
  • 720p HD video: 1.5 Mbps
  • 1080p HD video: 3 Mbps

However, group video calls require even higher bandwidth consumption. To make such experiences more immersive, even higher bit rates may be necessary, for instance, if using VR systems for attendance.

A simple QoE model may map the video bit rate of the current video session to a mean opinion score (MOS). [Lop18] provides a logistic regression MOS(x) depending on the video bit rate x in Mbps: f(x) = m_1 log x + m_2

Then, we can connect the QoE model with the energy consumption and CO₂ emissions model from above in the following way. We assume a user attending the conference for time t. With a video bit rate x, the emerging data traffic is v = x*t. Those input parameters are now used in the 1 Byte Model for a particular device (laptop, smartphone), type of network (wired, wifi, mobile), and country (EU, US, China).

Figure 1 shows the trade-off between the MOS and energy consumption (left y-axis). The energy consumption is mapped to CO₂ emission by assuming the corresponding parameter for the EU, and that the conference participants are all connected with a laptop. It can be seen that there is a strong increase in energy consumption and CO₂ emission in order to reach the best possible QoE. The MOS score of 4.75 is reached if a video bit rate of roughly 11 Mbps is used. However, with 4.5 Mbps, a MOS score of 4 is already reached according to that logarithmic model. This logarithmic behaviour is a typical observation in QoE and is connected to the Weber-Fechner law, see [Rei10]. As a consequence, we may significantly save energy and CO₂ when not providing the maximum QoE, but “only” good quality (i.e., MOS score of 4). The meaning of the MOS ratings is 5=Excellent, 4=Good, 3=Fair, 2=Poor, 1=Bad quality.

Figure 1: Trade-off between MOS and energy consumption or CO2 emission.

Figure 2, therefore, visualized the gain when delivering the video in lower quality and lower video bit rates. In fact, the gain compared to the efforts for MOS 5 are visualized. To get a better understanding of the meaning of those CO₂ numbers, we express the CO₂ gain now in terms of thousands of kilometers driving by car. Since the CO₂ emission depends on the share of renewable energies, we may consider different countries and the parameters as provided in [TSP20]. We see that ensuring each conference participant a MOS score of 4 instead of MOS 5 results in savings corresponding to driving approximately 40000 kilometers by car assuming the renewable energy share in the EU – this is the distance around the Earth! Assuming the energy share in China, this would save more than 90000 kilometers. Of course, you could also save 90 000 kilometers by walking – which requires however about 2 years non-stop with a speed of 5 km/h. Note that this large amount of CO₂ emission is calculated assuming a data rate of 15 Mbps over 5 days (and 6 hours per day), resulting in about 40.5 TB of data that needs to be transferred to the 200 conference participants.

Figure 2: Relating the CO2 emission in different countries for achieving this MOS to the distance by travelling in a car (in thousands of kilometers).

Discussions

Raising awareness of CO₂ emissions due to Internet service consumption is crucial. The abstract CO₂ emission numbers may be difficult to understand, but relating this to more common quantities helps to understand the impact individuals have. Of course, the provided numbers only give an impression, since the models are very simple and do not take into account various facets. However, the numbers nicely demonstrate the potential trade-off between QoE of end-users and sustainability in terms of energy consumption and CO₂ emission. In fact, [Gna21] conducted qualitative interviews and found that there is a lack of awareness of the environmental impact of digital applications and services, even for digital natives. In particular, an underlying issue is that there is a lack of understanding among end-users as to how Internet service delivery works, which infrastructure components play a role and are included along the end-to-end service delivery path, etc. Hence, the environmental impact is unclear for many users. Our aim is thus to contribute to overcoming this issue by raising awareness on this matter, starting with simplified models and visualizations.

[Gna21] also found that users indicate a certain willingness to make compromises between their digital habits and the environmental footprint. Given global climate changes and increased environmental awareness among the general population, such a trend in willingness to make compromises may be expected to further increase in the near future. Hence, it may be interesting for service providers to empower users to decide their environmental footprint at the cost of lower (yet still satisfactory) quality. This will also reduce the costs for operators and seems to be a win-win situation if properly implemented in Internet services and user interfaces.

Nevertheless, tremendous efforts are also currently being undertaken by Internet companies to become CO₂ neutral in the future. For example, Netflix claims in [Netflix2021] that they plan to achieve net-zero greenhouse gas emissions by the close of 2022. Similarly, also economic, societal, and environmental sustainability is seen as a key driver for 6G research and development [Mat21]. However, the time horizon is on a longer scope, e.g., a German provider claims they will reach climate neutrality for in-house emissions by 2025 at the latest and net-zero from production to the customer by 2040 at the latest [DT21]. Hence, given the urgency of the matter, end-users and all stakeholders along the service delivery chain can significantly contribute to speeding up the process of ultimately achieving net-zero greenhouse gas emissions.

References

  • [TSP19] The Shift Project, “Lean ict: Towards digital sobriety,” directed by Hugues Ferreboeuf, Tech. Rep., 2019, last accessed: March 2022. Available online (last accessed: March 2022)
  • [Yan19] M. Yan, C. A. Chan, A. F. Gygax, J. Yan, L. Campbell,A. Nirmalathas, and C. Leckie, “Modeling the total energy consumption of mobile network services and applications,” Energies, vol. 12, no. 1, p. 184, 2019.
  • [TSP20] Maxime Efoui Hess and Jean-Noël Geist, “Did The Shift Project really overestimate the carbon footprint of online video? Our analysis of the IEA and Carbonbrief articles”, The Shift Project website, June 2020, available online (last accessed: March 2022) PDF
  • [Kam20a] George Kamiya, “Factcheck: What is the carbon footprint of streaming video on Netflix?”, CarbonBrief website, February 2020. Available online (last accessed: March 2022)
  • [Kam20b] George Kamiya, “The carbon footprint of streaming video: fact-checking the headlines”, IEA website, December 2020. Available online (last accessed: March 2022)
  • [Feh11] Fehske, A., Fettweis, G., Malmodin, J., & Biczok, G. (2011). The global footprint of mobile communications: The ecological and economic perspective. IEEE communications magazine, 49(8), 55-62.
  • [Lop18]  J. P. López, D. Martín, D. Jiménez, and J. M. Menéndez, “Prediction and modeling for no-reference video quality assessment based on machine learning,” in 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), IEEE, 2018, pp. 56–63.
  • [Gna21] Gnanasekaran, V., Fridtun, H. T., Hatlen, H., Langøy, M. M., Syrstad, A., Subramanian, S., & De Moor, K. (2021, November). Digital carbon footprint awareness among digital natives: an exploratory study. In Norsk IKT-konferanse for forskning og utdanning (No. 1, pp. 99-112).
  • [Rei10] Reichl, P., Egger, S., Schatz, R., & D’Alconzo, A. (2010, May). The logarithmic nature of QoE and the role of the Weber-Fechner law in QoE assessment. In 2010 IEEE International Conference on Communications (pp. 1-5). IEEE.
  • [Netflix21] Netflix: “Environmental Social Governance 2020”,  Sustainability Accounting Standards Board (SASB) Report, (2021, March). Available online (last accessed: March 2022)
  • [Mat21] Matinmikko-Blue, M., Yrjölä, S., Ahokangas, P., Ojutkangas, K., & Rossi, E. (2021). 6G and the UN SDGs: Where is the Connection?. Wireless Personal Communications, 121(2), 1339-1360.
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