Samira Afzal (Alpen-Adria-Universität (AAU) Klagenfurt, Austria)
Radu Prodan (Alpen-Adria-Universität (AAU) Klagenfurt, Austria)
Christian Timmerer (Alpen-Adria-Universität (AAU) Klagenfurt and Bitmovin Inc., Austria)
Tobias Hoßfeld (University of Würzburg, Germany)
Christian Timmerer (Alpen-Adria-Universität (AAU) Klagenfurt and Bitmovin Inc., Austria)
Regarding the Intergovernmental Panel on Climate Change (IPCC) report in 2021 and Sustainable Development Goal (SDG) 13 “climate action”, urgent action is needed against climate change and global greenhouse gas (GHG) emissions in the next few years . This urgency also applies to the energy consumption of digital technologies. Internet data traffic is responsible for more than half of digital technology’s global impact, which is 55% of energy consumption annually. The Shift Project forecast  shows an increase of 25% in data traffic associated with 9% more energy consumption per year, reaching 8% of all GHG emissions in 2025.
Video flows represented 80% of global data flows in 2018, and this video data volume is increasing by 80% annually . This exponential increase in the use of streaming video is due to (i) improvements in Internet connections and service offerings , (ii) the rapid development of video entertainment (e.g., video games and cloud gaming services), (iii) the deployment of Ultra High-Definition (UHD, 4K, 8K), Virtual Reality (VR), and Augmented Reality (AR), and (iv) an increasing number of video surveillance and IoT applications . Interestingly, video processing and streaming generate 306 million tons of CO2, which is 20% of digital technology’s total GHG emissions and nearly 1% of worldwide GHG emissions .
While research has shown that the carbon footprint of video streaming has been decreasing in recent years , there is still a high need to invest in research and development of efficient next-generation computing and communication technologies for video processing technologies. This carbon footprint reduction is due to technology efficiency trends in cloud computing (e.g., renewable power), emerging modern mobile networks (e.g., growth in Internet speed), and end-user devices (e.g., users prefer less energy-intensive mobile and tablet devices over larger PCs and laptops). However, since the demand for video streaming is growing dramatically, it raises the risk of increased energy consumption.
Investigating energy efficiency during video streaming is essential to developing sustainable video technologies. The processes from video encoding to decoding and displaying the video on the end user’s screen require electricity, which results in CO2 emissions. Consequently, the key question becomes: “How can we improve energy efficiency for video streaming systems while maintaining an acceptable Quality of Experience (QoE)?”.
Challenges and Opportunities
In this section, we will outline challenges and opportunities to tackle the associated emissions for video streaming of (i) data centers, (ii) networks, and (iii) end-user devices  – presented in Figure 1.
Data centers are responsible for the video encoding process and storage of the video content. The video data traffic volume grows through data centers, driving their workloads with the estimated total power consumption of more than 1,000 TWh by 2025 . Data centers are the most prioritized target of regulatory initiatives. National and regional policies are established related to the growing number of data centers and the concern over their energy consumption .
- Suitable cloud services: Select energy-optimized and sustainable cloud services to help reduce CO2 emissions. Recently, IT service providers have started innovating in energy-efficient hardware by designing highly efficient Tensor Processing Units, high-performance servers, and machine-learning approaches to optimize cooling automatically to reduce the energy consumption in their data centers . In addition to advances in hardware designs, it is also essential to consider the software’s potential for improvements in energy efficiency .
- Low-carbon cloud regions: IT service providers offer cloud computing platforms in multiple regions delivered through a global network of data centers. Various power plants (e.g., fuel, natural gas, coal, wind, sun, and water) supply electricity to run these data centers generating different amounts of greenhouse gases. Therefore, it is essential to consider how much carbon is emitted by the power plants that generate electricity to run cloud services in the selected region for cloud computing. Thus, a cloud region needs to be considered by its entire carbon footprint, including its source of energy production.
- Efficient and fast transcoders (and encoders): Another essential factor to be considered is using efficient transcoders/encoders that can transcode/encode the video content faster and with less energy consumption but still at an acceptable quality for the end-user .
- Optimizing the video encoding parameters: There is a huge potential in optimizing the overall energy consumption of video streaming by optimizing the video encoding parameters to reduce the bitrates of encoded videos without affecting quality, including choosing a more power-efficient codec, resolution, frame rate, and bitrate among other parameters.
The next component within the video streaming process is video delivery within heterogeneous networks. Two essential energy consumption factors for video delivery are the network technology used and the amount of data to be transferred.
- Energy-efficient network technology for video streaming: the network technology used to transmit data from the data center to the end-users determine energy performance since the networks’ GHG emissions vary widely . A fiber-optic network is the most climate-friendly transmission technology, with only 2 grams of CO2 per hour of HD video streaming, while a copper cable (VDSL) generates twice as much (i.e., 4 grams of CO2 per hour). UMTS data transmission (3G) produces 90 grams of CO2 per hour, reduced to 5 grams of CO2 per hour when using 5G . Therefore, research shows that expanding fiber-optic networks and 5G transmission technology are promising for climate change mitigation .
- Lower data transmission: Lower data transmission drops energy consumption. Therefore, the amount of video data needs to be reduced without compromising video quality . The video data per hour for various resolutions and qualities range from 30 MB/hr for very low resolutions to 7 GB/hr for UHD resolutions. A higher data volume causes more transmission energy. Another possibility is the reduction of unnecessary video usage, for example, by avoiding autoplay and embedded videos. Such video content aims to maximize the quantity of content consumed. Broadcasting platforms also play a central role in how viewers consume content and, thus, the impact on the environment .
The last component of the video streaming process is video usage at the end-user device, including decoding and displaying the video content on the end-user devices like personal computers, laptops, tablets, phones, or television sets.
- End-user devices: Research works  show that the end-user devices and decoding hardware account for the greatest portion of energy consumption and CO2 emission in video streaming. Thus, most reduction strategies lay within the energy efficiency of the end-user devices, for instance, by improving screen display technologies or shifting from desktops to using more energy-efficient laptops, tablets, and smartphones.
- Streaming parameters: Energy consumption of the video decoding process depends on video streaming parameters similar to the end-user QoE. Thus, it is important to intelligently select video streaming parameters to optimize the QoE and power efficiency of the end-user device. Moreover, different underlying video encoding parameters also impact the video decodings’ energy usage.
- End-user device environment: A wide variety of browsers (including legacy versions), codecs, and operating systems besides the hardware (e.g., CPU, display) determine the final power consumption.
In this column, we argue that these challenges and opportunities for green video streaming can help to gain insights that further drive the adoption of novel, more sustainable usage patterns to reduce the overall energy consumption of video streaming without sacrificing end-user’s QoE.
End-to-end video streaming: While we have highlighted the main factors of each video streaming component that impact energy consumption to create a generic power consumption model, we need to study and holistically analyze video streaming and its impact on all components. Implementing a dedicated system for optimizing energy consumption may introduce additional processing on top of regular service operations if not done efficiently. For instance, overall traffic will be reduced when using the most recent video codec (e.g., VVC) compared to AVC (the most deployed video codec up to date), but its encoding and decoding complexity will be increased and, thus, require more energy.
Optimizing the video streaming parameters: There is a huge potential in optimizing the overall energy consumption for video service providers by optimizing the video streaming parameters, including choosing a more power-efficient codec implementation, resolution, frame rate, and bitrate, among other parameters.
GAIA: Intelligent Climate-Friendly Video Platform
Recently, we started the “GAIA” project to research the aspects mentioned before. In particular, the GAIA project researches and develops a climate-friendly adaptive video streaming platform that provides (i) 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; and (ii) reduced energy consumption and GHG emissions through advanced analytics and optimizations on all phases of the video delivery chain.
As shown in Figure 2, the research considered in GAIA comprises benchmarking, energy-aware and machine learning-based modeling, optimization algorithms, monitoring, and auto-tuning.
- Energy-aware benchmarking involves a functional requirement analysis of the leading project objectives, measurement of the energy for transcoding video tasks on various heterogeneous cloud and edge resources, video delivery, and video decoding on end-user devices.
- Energy-aware modelling and prediction use the benchmarking results and the data collected from real deployments to build regression and machine learning. The models predict the energy consumed by heterogeneous cloud and edge resources, possibly distributed across various clouds and delivery networks. We further provide energy models for video distribution on different channels and consider the relation between bitrate, codec, and video quality.
- Energy-aware optimization and scheduling researches and develops appropriate generic algorithms according to the requirements for real-time delivery (including encoding and transmission) of video processing tasks (i.e., transcoding) deployed on heterogeneous cloud and edge infrastructures.
- Energy-aware monitoring and auto-tuning perform dynamic real-time energy monitoring of the different video delivery chains for improved data collection, benchmarking, modelling and optimization.
GMSys 2023: First International ACM Green Multimedia Systems Workshop
Finally, we would like to use this opportunity to highlight and promote the first International ACM Green Multimedia Systems Workshop (GMSys’23). The GMSys’23 takes place in Vancouver, Canada, in June 2023 co-located with ACM Multimedia Systems 2023. We expect a series of at least three consecutive workshops since this topic may critically impact the innovation and development of climate-effective approaches. This workshop strongly focuses on recent developments and challenges for energy reduction in multimedia systems and the innovations, concepts, and energy-efficient solutions from video generation to processing, delivery, and consumption. Please see the Call for Papers for further details.
In both the GAIA project and ACM GMSys workshop, there are various actions and initiatives to put energy efficiency-related topics for video streaming on the center stage of research and development. In this column, we highlighted major video streaming components concerning their possible challenges and opportunities enabling energy-efficient, sustainable video streaming, sometimes also referred to as green video streaming. Having a thorough understanding of the key issues and gaining meaningful insights are essential for successful research.
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