EcoVideo: Sustainable Video Systems

• EcoVideo is a research paradigm that treats energy consumption, carbon intensity, and storage footprint as primary design objectives in video systems.
• It employs adaptive streaming, variable framerate encoding, and energy prediction methods to balance quality and efficiency under resource constraints.
• EcoVideo integrates systems co-design across cloud-edge collaboration, client displays, and machine-centric processing to minimize environmental impact.

Searching arXiv for EcoVideo and closely related green video streaming work. EcoVideo denotes a sustainability-oriented view of video systems in which energy consumption, carbon intensity, storage footprint, and perceptual sufficiency are treated as first-class design objectives across encoding, streaming, analysis, generation, and display. In the recent literature, the term appears both as a broad framing for green multimedia and adaptive video systems and as the title of a specific cloud–edge framework for Diffusion Transformer video generation that uses entropy-orchestrated dynamic inter-frame decoupling to improve quality–efficiency trade-offs under bandwidth and compute constraints (Chen et al., 29 Jun 2026). Across adjacent work, EcoVideo therefore refers not to a single method, but to a technical agenda: exploiting redundancy, perceptual tolerance, environmental context, and systems co-design to reduce energy and storage costs while preserving quality of experience or downstream task utility (Afzal et al., 2024).

1. EcoVideo as a research paradigm

The EcoVideo perspective is grounded in the observation that video systems consume energy across multiple stages, including encoding, storage, retrieval, decoding, and display, and that the environmental impact of these stages depends on both resource use and electricity carbon intensity (Afzal et al., 2024). A survey on the energy consumption and environmental impact of video streaming identifies three notable weaknesses requiring further research for improved energy efficiency: fixed bitrate ladders in HTTP live streaming, inefficient hardware utilization of existing video players, and lack of comprehensive open energy measurement datasets covering various device types and coding parameters (Afzal et al., 2024). This positions EcoVideo as a systems problem rather than a single codec or model-selection problem.

A second strand of the literature frames green multimedia through compact representation. The survey on compact visual data representation for green multimedia argues that visual data should often be represented for knowledge extraction rather than raw signal reconstruction, emphasizing green storage through compression, green processing by direct feature usage, and dynamic green operations that adapt to task needs, network conditions, and available resources (Chen et al., 2024). This suggests an EcoVideo agenda in which rate–distortion tradeoffs are extended toward rate–distortion–task–energy tradeoffs.

The same agenda appears in adaptive streaming, cloud-edge collaboration, and client-side playback. Work on energy-rate-quality tradeoffs shows that codec sustainability is a three-way optimization problem over energy, rate, and quality, rather than a simple race toward maximum compression (Katsenou et al., 2022). Work on subscription design for carbon-aware streaming further extends EcoVideo to user incentives, proposing a practical 2-tier subscription model with a discount and carbon rewards, where the provider may reduce the quality for up to a maximum percentage of videos within a time period (Siris et al., 9 Apr 2026).

2. Streaming, encoding, and delivery optimization

A central EcoVideo theme is that standard adaptive streaming is not energy-optimal because it usually selects content based only on network bandwidth. EVSO, “Environment-aware Video Streaming Optimization of Power Consumption,” extends the media presentation description to allow additional consideration of the user's battery status and applies adaptive frame rates to parts of videos with a little degradation of the user experience (Park et al., 2019). Its server preprocesses a video into several processed videos according to the similarity intensity of each part of the video and then provides the client with the processed video suitable for the network bandwidth and the battery status of the client's mobile device. Implemented on the H.264/AVC encoder, EVSO reports that energy consumption is reduced by 22% on average and up to 27% while maintaining the quality of the user experience (Park et al., 2019).

Variable-framerate ladder design extends this idea from client adaptation to live encoding. The content-adaptive variable framerate scheme for green live streaming defines two modes. CVFR-ECO predicts the optimized framerate for each representation in the bitrate ladder, while CVFR-HQ predicts each representation's optimized framerate-encoding preset pair using low-complexity discrete cosine transform energy-based spatial and temporal features (Menon et al., 2023). CVFR-ECO yields an average PSNR and VMAF increase of 0.02 dB and 2.50 points, respectively, for the same bitrate, and also yields an average encoding and storage energy consumption reduction of 34.54% and 76.24%, considering a just noticeable difference of six VMAF points. CVFR-HQ yields an average increase in PSNR and VMAF of 2.43 dB and 10.14 points, respectively, for the same bitrate, and an average reduction in storage energy consumption of 83.18% under the same JND assumption (Menon et al., 2023).

More recent work addresses energy prediction directly. “Predicting Encoding Energy from Low-Pass Anchors for Green Video Streaming” uses one low-cost anchor representation, defined as the lowest tested resolution and highest tested quantization parameter, to predict encoding energy, decoding energy, and quality for other representations (Azimi et al., 1 Nov 2025). On 100 Inter4K sequences with VVenC and VVdeC, the method reports that for an average VMAF score reduction of only 1.68, which stays below the Just Noticeable Difference threshold, it achieves 51.22% encoding energy savings and 53.54% decoding energy savings compared to a scenario with no quality degradation (Azimi et al., 1 Nov 2025). This suggests that EcoVideo optimization can be formulated as a constrained search over resolution and quantization parameter using predicted energy and quality rather than exhaustive measurement.

At a broader codec-selection level, “Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs” compares SVT-AV1, VVenC/VVdeC, VP9, and x.265, measuring both encoding and decoding energy (Katsenou et al., 2022). Under the tested software implementations and CPU conditions, x.265 is the lowest-energy option, while SVT-AV1 offers the best tradeoff between energy consumption and quality (Katsenou et al., 2022). A plausible implication is that EcoVideo systems cannot choose codecs by BD-rate or VMAF alone; codec choice interacts with where energy is consumed, whether encoding is amortized across many viewers, and whether endpoint or provider energy dominates.

3. Complexity analysis and compact representation

EcoVideo depends not only on better representations but also on making representation analysis itself energy-efficient. “Green Video Complexity Analysis for Efficient Encoding in Adaptive Video Streaming” critiques Spatial Information and Temporal Information as weak proxies for coding decisions and instead uses DCT-energy features through the Video Complexity Analyzer (Menon et al., 2023). VCA extracts seven blockwise complexity features, including luma texture energy $E_Y$, the gradient of luma texture energy $h$, luma luminescence $L_Y$, chroma texture energies $E_U$ and $E_V$, and chrominance values $L_U$ and $L_V$. The optimized VCA v2.0 combines multithreading, x86 SIMD acceleration, and low-pass DCT optimization, reaching up to 292.68 fps and consuming 97.06% less energy than the reference SITI implementation on UHD 8-bit videos (Menon et al., 2023).

The same paper reports that for all-intra encoding, the average Pearson Correlation Coefficient between SI and bitrate is 0.28, whereas the average PCC between $E_Y$ and bitrate is 0.86 (Menon et al., 2023). This indicates that EcoVideo feature extraction should be aligned with coding behavior, not just perceptual saliency in the abstract. A plausible implication is that green adaptive systems benefit twice: they reduce the overhead of analysis itself, and they make better encoding decisions once that analysis is available.

Representation-level compression can also redefine what counts as “video.” “Video Compression for Spatiotemporal Earth System Data” treats remote sensing and climate arrays as videos, introducing xarrayvideo, a Python library for compressing multichannel spatiotemporal datasets by encoding them as videos (Pellicer-Valero et al., 24 Jun 2025). On DynamicEarthNet, DeepExtremeCubes, ERA5, and SimpleS2, it reports PSNRs of 55.86, 40.60, 46.58, and 43.23 dB at 0.1 bits per pixel per band and 65.91, 54.28, 62.90, and 55.04 dB at 1 bpppb (Pellicer-Valero et al., 24 Jun 2025). No performance loss is observed when compressed versions of DeepExtremeCubes and DynamicEarthNet are used in their respective deep learning-based downstream tasks (Pellicer-Valero et al., 24 Jun 2025). This extends EcoVideo from human-viewed streaming to environmental data pipelines, where video codecs become tools for scientific storage and redistribution.

4. Edge, cloud, and machine-centric video systems

EcoVideo is equally relevant when the consumer of video is a machine rather than a human viewer. EKO, “Adaptive Sampling of Compressed Video Data,” is a storage engine for efficiently managing video data through an unsupervised adaptive sampling algorithm and a machine-oriented compressed representation (Bang et al., 2021). It improves F1-score by up to 9% compared to the next best performing state-of-the-art unsupervised sampling algorithms, reduces query execution time by 3X, and reduces memory footprint by 10X in comparison to a widely-used, traditional video storage format (Bang et al., 2021). The broader EcoVideo lesson is that a green system may avoid full decode entirely, choosing a representation optimized for inference workloads rather than playback.

A related edge-centric example comes from ecological sensing. “A motion-based compression algorithm for resource-constrained video camera traps” introduces EcoMotionZip, which identifies and stores only image regions depicting motion relevant to pollination monitoring (Ratnayake et al., 2024). On six real-world datasets, it reduces overall data size by an average of 87%, with up to 97.56% file-size reduction on the strawberry crop dataset, while preserving critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage (Ratnayake et al., 2024). This is a task-oriented EcoVideo design in which biologically irrelevant pixels and idle frames are aggressively removed because the downstream objective is behavioral monitoring, not full-scene reconstruction.

Cloud-edge generation adds a different systems dimension. “EcoVideo: Entropy-Orchestrated Video Generation Paradigm in Cloud-Edge Dynamics” addresses the latency of Diffusion Transformer video generation by changing the collaboration axis from denoising-step partitioning to frame partitioning (Chen et al., 29 Jun 2026). Early-stage self-attention entropy is used as a training-free estimate of frame-wise information density; a cloud large model denoises sparse high-entropy keyframes; and an edge lightweight model reconstructs the remaining frames via motion-aware interpolation with refinement for temporal stability (Chen et al., 29 Jun 2026). The method adapts the keyframe budget and edge refinement depth to bandwidth and compute availability and reports improved quality–efficiency trade-offs and up to 2.9x end-to-end speedup in low-bandwidth, compute-limited edge settings (Chen et al., 29 Jun 2026). Unlike green streaming papers that focus on bitrate ladders or codec choice, this EcoVideo instance targets generative latency and communication volume in cloud-edge inference.

5. Display, client energy, and end-to-end system tradeoffs

EcoVideo literature repeatedly shows that display and client behavior can dominate environmental impact. The survey on streaming energy consumption notes that end-user devices account for 72% of energy consumption, followed by 23% for data transmission and 5% for data centers, and that display energy is often dominant during playback (Afzal et al., 2024). It further cites large device-dependent differences, including that a 50-inch TV may have a carbon footprint 4.5× that of a laptop and 90× that of a smartphone (Afzal et al., 2024).

User behavior can amplify these differences. “All you can stream: Investigating the role of user behavior for greenhouse gas intensity of video streaming” reports that climate intensity differs by roughly a factor of 10 between choosing a smart TV and a smartphone, and identifies setting a low resolution as default as one factor that can be tackled from provider side to reduce overall energy demand at the user side (Suski et al., 2020). The same study models weekly streaming GWP as the sum of production-related device emissions, device operation emissions, and data-traffic emissions, explicitly linking environmental assessment to duration, device choice, and resolution setting (Suski et al., 2020). This suggests that EcoVideo must account for defaults and interface design, not only infrastructure-side optimization.

Display-aware content transformation appears as another end-to-end lever. “3R-INN: How to be climate friendly while consuming/delivering videos?” proposes a single light invertible network that rescales a high-resolution grainy image to a lower resolution, removes film grain, and reduces power consumption when displayed (Ameur et al., 2024). The paper reports significant energy savings for encoding (78%), decoding (77%), and rendering (5% to 20%), while also allowing recovery of either the high-resolution grainy original image or a grain-free version without transmitting auxiliary data (Ameur et al., 2024). In practical terms, this is an EcoVideo design in which content is transformed before coding so that encoding, transmission, decoding, and display all become cheaper.

The most general takeaway from these results is that EcoVideo is inherently end-to-end. A codec that lowers bitrate but raises decode energy may not be greener on battery-powered clients; a streaming policy that lowers network traffic may still be suboptimal if the display dominates; and a player that underutilizes hardware acceleration can erase gains obtained elsewhere (Afzal et al., 2024).

6. Carbon-aware economics, user incentives, and open problems

Some EcoVideo work moves beyond technical adaptation to incentive design. The 2-tier subscription model with carbon-aware rewards investigates incentives that depend on the energy consumption of segments in the end-to-end video delivery path, the carbon intensity, and user type (Siris et al., 9 Apr 2026). It concludes that it is preferable to offer subscriptions where the reduced-quality tier is set one resolution level below the resolution required for maximum user satisfaction, and that when a video is streamed from a local data center, the maximum percentage of videos streamed at a lower quality depends solely on the carbon intensity and the average intensity cap (Siris et al., 9 Apr 2026). This suggests that EcoVideo can be operationalized not only by algorithmic adaptation but also by product design, contracts, and user segmentation.

A complementary social-media perspective appears in work on short environmental videos. “Video Popularity in Social Media: Impact of Emotions, Raw Features and Viewer Comments” finds that viewer comments and reactions predict popularity better than raw video features, with accuracy 0.8 versus 0.67, and that sadness in posts is positively correlated with likes while sentiment scores show negative correlations with likes and shares (Ziyada et al., 2024). Although this is not an energy paper, it is relevant to EcoVideo as a communication problem: environmentally themed video content circulates according to emotional framing and audience response, which may shape the reach of sustainability messages even when the underlying media system is technically efficient.

Several open problems recur across the literature. Fixed bitrate ladders remain wasteful in both VoD and live settings (Afzal et al., 2024). Existing video players still show inefficient hardware utilization (Afzal et al., 2024). Open, comprehensive energy measurement datasets remain limited (Afzal et al., 2024). In cloud-edge generation, entropy-based inter-frame decoupling depends on temporal redundancy and may struggle with extremely fast motion, heavy occlusion, or abrupt scene changes (Chen et al., 29 Jun 2026). In machine-oriented storage and sensing systems, selective representations can fragment tracks or omit stationary but semantically important events (Bang et al., 2021, Ratnayake et al., 2024). This suggests that EcoVideo is less a solved architecture than an optimization frontier spanning rate, distortion, task accuracy, latency, energy, and carbon intensity.

Taken together, the literature defines EcoVideo as a technical field organized around a single principle: video systems should expend resources only where perceptual fidelity, user utility, or downstream inference actually requires them. Whether implemented through adaptive frame rates, DCT-energy complexity analysis, machine-centric storage, invertible display-aware preprocessing, carbon-aware subscriptions, or entropy-orchestrated cloud-edge generation, EcoVideo replaces throughput-maximizing video design with resource-aware, context-aware, and often task-aware video design (Park et al., 2019, Menon et al., 2023, Chen et al., 29 Jun 2026).