Point clouds are a promising video representation for virtual and augmented reality. Their high-bitrate, however, has so far limited the practicality of live streaming systems. In this work, we leverage the delivery timeout feature within the Media Over QUIC protocol to perform implicit server-side adaptation based on an application's latency target. Through experimentation with several publisher and network configurations, we demonstrate that our system unlocks a unique trade-off on a per-client basis: applications with lower latency requirements will receive lower-quality video, while applications with more relaxed latency requirements will receive higher-quality video.

Global climate change is a significant environmental concern, and reducing greenhouse gas emissions is crucial to mitigating this issue. Moreover, there is a need to exploit a prediction-based method to assess the future requirements of applications and (re-)schedule them with the aim of reducing completion time and energy consumption. Therefore, we consider the stochastic requirements of users and investigate an Energy-aware Prediction-based scheduling of dataflow processing on the cloud, fog, and edge method, named EPreMatch, for microservice scaling by applying a machine learning (ML) model based on gradient boosting regression (GBR) and scheduling due to ranking and matching game principles. Firstly, EPreMatch predicts the number of microservice replicas using GBR. Then, the ranking method orders the microservice replicas and devices based on completion times and energy consumption. Thereafter, the EPreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis reveals lower completion times, energy consumption, and CO2emission compared to a related prediction-based scheduling method.

Global climate change is a significant environmental concern, and reducing greenhouse gas emissions is crucial to mitigating this issue. Moreover, there is a need to exploit a prediction-based method to assess the future requirements of applications and (re-)schedule them with the aim of reducing completion time and energy consumption. Therefore, we consider the stochastic requirements of users and investigate an Energy-aware Prediction-based scheduling of dataflow processing on the cloud, fog, and edge method, named EPreMatch, for microservice scaling by applying a machine learning (ML) model based on gradient boosting regression (GBR) and scheduling due to ranking and matching game principles. Firstly, EPreMatch predicts the number of microservice replicas using GBR. Then, the ranking method orders the microservice replicas and devices based on completion times and energy consumption. Thereafter, the EPreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis reveals lower completion times, energy consumption, and CO2emission compared to a related prediction-based scheduling method.

The environmental impact of video streaming is gaining more attention due to its growing share in global internet traffic and energy consumption. To support accurate and transparent sustainability assessments, we present SEED (Streaming Energy and Emission Dataset): an open dataset for estimating energy usage and CO2emissions in adaptive video streaming. SEED comprises 500 video segments. It provides segment-level measurements of energy consumption and emissions for two primary stages: provisioning, which encompasses encoding and storage on cloud infrastructure; and end-user consumption, including network interface retrieval, video decoding, and display on end-user devices. The dataset covers multiple codecs (AVC, HEVC), resolutions, bitrates, cloud instance types, and geographic regions, reflecting real-world variations in computing efficiency and regional carbon intensity. By combining empirical benchmarks with component-level energy models, SEED enables detailed analysis and supports the development of energy- and emission-aware adaptive bitrate (ABR) algorithms. The dataset is publicly available at: https://github.com/cd-athena/SEED.

Video streaming has evolved from push-based, broad-/multicasting approaches with dedicated hard-/software infrastructures to pull-based unicast schemes utilizing existing Web-based infrastructure to allow for better scalability. In this article, we provide an overview of the foundational principles of HTTP Adaptive Streaming (HAS), from video encoding to end user consumption, while focusing on the key advancements in adaptive bitrate algorithms, Quality of Experience (QoE), and energy efficiency. Furthermore, the article highlights the ongoing challenges of optimizing network infrastructure, minimizing latency, and managing the environmental impact of video streaming. Finally, future directions for HAS, including immersive media streaming and neural network-based video codecs, are discussed, positioning HAS at the forefront of next-generation video delivery technologies.

Addressing climate change requires a global decrease in greenhouse gas (GHG) emissions. In today's digital landscape, video streaming significantly influences internet traffic, driven by the widespread use of mobile devices and the rising popularity of streaming platforms. This trend emphasizes the importance of evaluating energy consumption and the development of sustainable and eco-friendly video streaming solutions with a low Carbon Dioxide (CO2) footprint. We developed a specialized tool, released as an open-source library called GREEM, addressing this pressing concern. This tool measures video encoding and decoding energy consumption and facilitates measurement testbeds. It monitors the computational impact on hardware resources and offers various analysis cases. GREEM is helpful for developers, researchers, service providers, and policymakers interested in minimizing the energy consumption of video encoding and streaming.

Video streaming constitutes 65 % of global internet traffic, prompting an investigation into its energy consumption and CO2 emissions. Video encoding, a computationally intensive part of streaming, has moved to cloud computing for its scalability and flexibility. However, cloud data centers' energy consumption, especially video encoding, poses environmental challenges. This paper presents VEED, a FAIR Video Encoding Energy and CO2 Emissions Dataset for Amazon Web Services (AWS) EC2 instances. Additionally, the dataset also contains the duration, CPU utilization, and cost of the encoding. To prepare this dataset, we introduce a model and conduct a benchmark to estimate the energy and CO2 emissions of different Amazon EC2 instances during the encoding of 500 video segments with various complexities and resolutions using Advanced Video Coding (AVC) and High-Efficiency Video Coding (HEVC). VEED and its analysis can provide valuable insights for video researchers and engineers to model energy consumption, manage energy resources, and distribute workloads, contributing to the sustainability of cloud-based video encoding and making them cost-effective. VEED is available at https://github.com/cd-athena/VEED-dataset.

HTTP Adaptive Streaming (HAS) has emerged as the predominant solution for delivering video content on the Internet. The urgency of the climate crisis has accentuated the demand for investigations into the environmental impact of HAS techniques. In HAS, clients rely on adaptive bitrate (ABR) algorithms to drive the quality selection for video segments. Focusing on maximizing video quality, these algorithms often prioritize maximizing video quality under favorable network conditions, disregarding the impact of energy consumption. To thoroughly investigate the effects of energy consumption, including the impact of bitrate and other video parameters such as resolution and codec, further research is still needed. In this paper, we propose COCONUT, a COntent COnsumption eNergy measUrement daTaset for adaptive video streaming collected through a digital multimeter on various types of client devices, such as laptop and smartphone, streaming MPEG-DASH segments. Furthermore, we analyze the dataset and find insights into the influence of multiple codecs, various video encoding parameters, such as segment length, framerate, bitrates, and resolutions, and decoding type, i.e., hardware or software, on energy consumption. We gather and categorize these measurements based on segment retrieval through the network interface card (NIC), decoding, and rendering. Additionally, we compare the impact of different HAS players on energy consumption. This research offers valuable perspectives on the energy usage of streaming devices, which could contribute to creating a media consumption experience that is both more sustainable and resource-efficient. Dataset URL: https://athena.itec.aau.at/coconut/.

In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30fps and 60fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32 and 37. In addition, for AV1, three additional QPs of 35, 46 and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient streaming.Dataset URL: https://github.com/cd-athena/MVCD.

Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA’s effectiveness, yielding an average decoding energy reduction of up to 13.45 %, with minimal VMAF reduction of 0.33 points at a low-quality degradation threshold, compared to the default 60 fps encoding. Even at an aggressive threshold, DECODRA achieves significant energy savings of 13.45 % while only reducing VMAF by 2.11 points. In this way, DECODRA extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.

HTTP Adaptive Streaming (HAS) methods divide a video into smaller segments, encoded at multiple pre-defined bitrates to construct a bitrate ladder. Bitrate ladders are usually optimized per title over several dimensions, such as bitrate, resolution, and framerate. This paper adds a new dimension to the bitrate ladder by considering the energy consumption of the encoding process. Video encoders often have multiple pre-defined presets to balance the trade-off between encoding time, energy consumption, and compression efficiency. Faster presets disable certain coding tools defined by the codec to reduce the encoding time at the cost of reduced compression efficiency. Firstly, this paper evaluates the energy consumption and compression efficiency of different x265 presets for 500 video sequences. Secondly, optimized presets are selected for various representations in a bitrate ladder based on the results to guarantee a minimal drop in video quality while saving energy. Finally, a new per title model, which optimizes the trade-off between compression efficiency and energy consumption, is proposed. The experimental results show that decreasing the VMAF score by 0.15 and 0.39 while choosing an optimized preset results in encoding energy savings of 70% and 83%, respectively.

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., High Efficiency Video Coding (HEVC), Alliance for Open Media Video 1 (AV1)) that lie below the predicted rate-distortion curve of the Advanced Video Coding (AVC) codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF score of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.

Demand for wireless video streaming services increases with users expecting to access high-quality video streaming experiences. Ensuring Quality of Experience (QoE) is quite challenging due to varying bandwidth and time constraints. Since most of today’s mobile devices are equipped with multiple network interfaces, one promising approach is to benefit from multipath communications. Multipathing leads to higher aggregate bandwidth and distributing video traffic over multiple network paths improves stability, seamless connectivity, and QoE. However, most of current transport protocols do not match the requirements of video streaming applications or are not designed to address relevant issues, such as networks heterogeneity, head-of-line blocking, and delay constraints. In this comprehensive survey, we first review video streaming standards and technology developments. We then discuss the benefits and challenges of multipath video transmission over wireless. We provide a holistic literature review of multipath wireless video streaming, shedding light on the different alternatives from an end-to-end layered stack perspective, reviewing key multipath wireless scheduling functions, unveiling trade-offs of each approach, and presenting a suitable taxonomy to classify the state-of-the-art. Finally, we discuss open issues and avenues for future work.

This paper provides an overview of the open Video Complexity Dataset (VCD) which comprises 500 Ultra High Definition (UHD) resolution test video sequences. These sequences are provided at 24 frames per second (fps) and stored online in losslessly encoded 8-bit 4:2:0 format. In this paper, all sequences are characterized by spatial and temporal complexities, rate-distortion complexity, and encoding complexity with the x264 AVC/H.264 and x265 HEVC/H.265 video encoders. The dataset is tailor-made for cutting-edge multimedia applications such as video streaming, two-pass encoding, per-title encoding, scene-cut detection, etc. Evaluations show that the dataset includes diversity in video complexities. Hence, using this dataset is recommended for training and testing video coding applications. All data have been made publicly available as part of the dataset, which can be used for various applications.

Video streaming is the dominating traffic in today’s data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed in seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%– 50% lower completion time and 5%–60% reduced total cost.

Bandwidth constraints and high end-to-end delays are real challenges for achieving and sustaining high quality mobile video streaming services. Diverse multipath transmission techniques are being investigated as possible solutions, since recent developments have enabled mobile devices users to receive video data simultaneously over multiple interfaces (e.g., LTE and WiFi). While some multipath protocols have been recently standardized for this purpose (e.g., MPTCP), being network layer protocols they cannot properly handle challenging transmission scenarios subject to packet losses and congestion, such as lossy wireless channels. In this work, we adopt the MPEG Media Transport (MMT) protocol to propose an improvement for mobile multipath video streaming solutions. MMT is an application layer protocol with inherent hybrid media delivery properties. We propose a novel path-and-content-aware scheduling strategy for MMT by means of full cooperation between network metrics and video content features. Our strategy provides better models to adaptively cope with unstable communication channel conditions and to improve the final user quality of experience (QoE). For the experimental evaluation, we used NS3-DCE to simulate a realistic multipath network scenario which includes channel error models and background traffic. Results for two video sequences are presented in terms of PSNR, SSIM, goodput, delay and packet loss rates. When compared with a simple scheduling strategy for the traditional multipath MMT, our approach yields significant packet loss rate reductions (~90%) and video quality improvements of around 12 dB for PSNR and 0.15 for SSIM.

As the density of wireless networks continues to grow with more clients, more base stations, and more traffic, designing cost-effective wireless solutions with efficient resource usage and ease to manage is an increasing challenging task due to the overall system complexity. A number of vendors offer scalable and high-performance wireless networks but at a high cost and commonly as a single-vendor solution, limiting the ability to innovate after roll-out. Recent Software-Defined Networking (SDN) approaches propose new means for network virtualization and programmability advancing the way networks can be designed and operated, including user-defined features and customized behaviour even at run-time. However, means for rapid prototyping and experimental evaluation of SDN for wireless environments are not yet available. This paper introduces Mininet-WiFi as a tool to emulate wireless OpenFlow/SDN scenarios allowing high-fidelity experiments that replicate real networking environments. Mininet-WiFi augments the well-known Mininet emulator with virtual wireless stations and access points while keeping the original SDN capabilities and the lightweight virtualization software architecture. We elaborate on the potential applications of Mininet-Wifi and discuss the benefits and current limitations. Two use cases based on IEEE 802.11 demonstrate available functionality in our open source developments.