CSI Magazine Award for Best Sustainability Project or Initiative to GAIA team

We are proud to announce that our project GAIA  was highly commended at the CSI Magazine Awards for best sustainability initiative!  This award recognizes some of the work taking place in the video streaming process to reduce the environmental impact of technology and to drive the world’s green transition.

GAIA (Greek goddess of Earth, mother of all life, personification of the Earth) is a cooperative project between Bitmovin and Alpen-Adria-Universität Klagenfurt (AAU) that aims to enable development of more climate-friendly video streaming platforms by providing complete energy awareness and accountability throughout the entire video delivery chain. GAIA uses innovative technologies such as  Video Encoding Matching-based Model for Cloud and Edge Computing Instances, Green Per-Title Encoding,  and Designing Energy Efficient Player to minimizing the energy consumption and carbon footprint while maintaining the Quality of Experience for users.

We would like to thank the CSI Magazine Awards for this recognition and our partner, Bitmovin, and collaborators for their contribution and support to GAIA. We hope that GAIA will inspire more initiatives and projects that aim to make a positive difference for our planet.

You can find more information about GAIA on our website

13th International Conference on Computer and Knowledge Engineering (ICCKE)

November 1-2, 2023 | Mashad, Iran

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Reza Saeedinia (University of Tehran), S. Omid Fatemi (University of Tehran), Daniele Lorenzi (AAU, Austria), Farzad Tashtarian (AAU, Austria),  Christian Timmerer (AAU, Austria)

Live user-generated content (UGC) has increased significantly in video streaming applications. Improving the quality of experience (QoE) for users is a crucial consideration in UGC live streaming, where a user can be both a subscriber and a streamer. Resource allocation is an NP-complete task in UGC live streaming due to many subscribers and streamers with varying requests, bandwidth limitations, and network constraints. In this paper, to decrease the execution time of the resource allocation algorithm, we first process streamers’ and subscribers’ requests and then aggregate them into a limited number of groups based on their preferences. Second, we
perform resource allocation for these groups that we call communities. We formulate the resource allocation problem for communities into an optimization problem. With an efficient aggregation of subscribers and streamers at the core of the proposed architecture, the computational complexity of the optimization problem is reduced, consequently improving QoE. This improvement occurs because of the prompt reaction to the bandwidth fluctuations and, subsequently, appropriate resource allocation by the proposed model. We conduct experiments in various scenarios. The results show an average of 41% improvement in execution time. To evaluate the impact of bandwidth fluctuations on the proposed algorithm, we employ two network traces: AmazonFCC and NYUBUS. The results show 4%, and 28% QoE improvement in a scenario with 5
streamers over the AmazonFCC and the NYUBUS network traces, respectively

18th USENIX Symposium on Networked Systems Design and Implementation

April 16–18, 2024 | Santa Clara, CA, USA

Conference website

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Farzad Tashtarian (AAU, Austria),  Abdelhak Bentaleb (Concordia University), Hadi Amirpour (AAU, Austria) , Sergey Gorinsky (IMDEA Networks Institute),  Junchen Jiang (University of Chicago), Hermann Hellwagner (AAU, Austria) , Christian Timmerer (AAU, Austria)

Live streaming of segmented videos over the Hypertext Transfer Protocol (HTTP) is increasingly popular and serves heterogeneous clients by offering each segment in multiple representations. A bitrate ladder expresses this choice as an ordered list of bitrate-resolution pairs. Whereas existing solutions for HTTP-based live streaming use a static bitrate ladder, the fixed ladders struggle to appropriately accommodate the dynamics in the video content and network-conditioned client capabilities. This paper proposes ARTEMIS as a practical scalable alternative that dynamically configures the bitrate ladder depending on the content complexity, network conditions, and clients’ statistics. ARTEMIS seamlessly integrates with the end-to-end streaming pipeline and operates transparently to video encoders and clients. We develop a cloud-based implementation of ARTEMIS and conduct extensive real-world and trace-driven experiments. The experimental comparison vs. existing prominent bitrate ladders demonstrates that live streaming with ARTEMIS outperforms all baselines, reduces encoding computation by 25%, end-to-end latency by 18%, and increases quality of experience (QoE) by 11%.

IEEE ICME 2023
Monday, July 10, 2023
https://www.2023.ieeeicme.org/tutorial-prop.php

Lectures:

• Hadi Amirpour, AAU, AT
• Yutiy Reznik, Brightcove, USA
• Nabajeet Barman,  Brightcove
• Christian Timmerer, AAU, AT

Abstract: The applications of video streaming are the primary drivers of Internet traffic, as over 82% of IP traffic in 2022. The prevailing technology to stream live and video-on-demand (VoD) content over the Internet is HTTP Adaptive Streaming (HAS). In HAS, the content is encoded at multiple representations (bitrate-resolution pairs), and delivered incrementally using segments (or chunks) of encoded representations. This allows for dynamic selection of different representations during playback, and the HAS model supports delivery under different and changing network- and device-specific conditions. As the demand for video streaming applications is on the rise, improved streaming methods, as well as video codecs and video content optimization algorithms, are being developed to meet this demand. This tutorial first presents a detailed overview of the existing file formats, protocols (HLS, DASH, CMAF), video codecs (H.264, HEVC, AV1, VVC), and quality assessment methods (PSNR, SSIM, VMAF, P.1203, CTA2066) used for streaming. Particular emphasis will be given to new video codecs, such as Versatile Video Coding (VVC), and their features applicable to streaming. We will then focus on recent advances in video encoding optimizations for HAS streaming. We will then introduce per-title, content-aware, and context-aware encoding methods, which optimize bitrate ladders based on the characteristics of videos and other contexts. It will also be presented how representations are selected in a way that bitrate ladders are optimized over various dimensions, including spatial resolution, frame rate, energy consumption, device type, and network condition. Additionally, methods for reducing the latency of dynamic ladder generation and fast multi-rates encoding will be covered. Finally, we will discuss various areas of recent progress in the design of streaming systems, including end-to-end modelling, optimization, and analysis, and highlight remaining open problems in the field.

IEEE Network Special Issue on

Advancements in Network-Assisted Video Streaming: Optimization and Performance Analysis

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Call for Papers

Network-assisted video streaming has become a substantial part of modern multimedia applications, enabling users to access high-quality video content over different networks, including the Internet and wireless networks. Efficiently delivering video content over networks poses numerous challenges, such as limited bandwidth, varying network conditions, heterogeneous end devices, and diverse user preferences. Network-assisted video streaming approaches leverage modern networking technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, to not only improve the users’ Quality of Experience (QoE) but also enhance network utilization.

This special issue aims to explore the latest advancements in network-assisted video streaming, with a specific focus on optimization techniques and comprehensive performance analysis, by exploring emerging trends and innovations, including novel approaches that leverage Artificial Intelligence (AI) techniques, machine learning algorithms, and data-driven optimization methods to enhance the streaming experience. Furthermore, contributions related to the integration of edge computing, Virtual Reality (VR), Augmented Reality (AR), and volumetric video streaming will be welcomed. Since understanding the performance of network-assisted video streaming systems is important for assessing their effectiveness and identifying areas for improvement, the research articles should cover both experimental and theoretical aspects, utilizing real-world datasets, simulation frameworks, analytical models, and conducting real-world experiments.

The research and advancements of this special issue will have a significant impact on the design, implementation, and operation of video streaming systems. The findings will provide valuable insights to network operators, content providers, researchers, and developers, enabling them to optimize their systems for enhanced user experience. Additionally, the knowledge gained from this special issue will contribute to the development of standards and best practices for network-assisted video streaming, benefiting the broader multimedia community. IEEE Network, the flagship magazine on networking technologies, is a perfect venue for publishing this special issue. We believe that our special issue covers a number of key aspects and emerging topics that are of interest to the readers of the IEEE Network.

This special issue is to publish original research and review articles that should be comprehensive to all readers of the IEEE Network Magazine, regardless of their specialty. This SI aims to bring together researchers and developers working on all aspects of video streaming, in particular network-assisted concepts backed up by experimental evidence. Potential topics include but are not limited to the following:

• Design, analysis, and evaluation of network-assisted multimedia system architectures
• Using AI/ML at the network edge and the cloud for supporting video streaming
• AI/ML-enabled caching of video chunks
• Network-assisted/AI-based resource allocation for video streaming
• Experience and lessons learned by deploying large-scale network-assisted video streaming
• Internet measurement and modeling for enhancing QoE in video streaming applications
• Network aspects in video streaming: cloud computing, virtualization techniques, network control, and management, including SDN, NFV, and network programmability
• Topics at the intersection of energy-efficient computing and networking for video streaming
• Machine learning for improving QoE in video streaming applications
• Machine learning for traffic engineering and congestion control for video streaming
• AI/ML-based solutions for supporting streaming applications’ high-speed user mobility
• Big data analytics at the network edge to assess viewer experience of adaptive video
• Advanced network-based techniques for point clouds, light fields, and immersive video
• Using AI/ML techniques for optimizing Interactive Streaming and User-Generated Content
• The tradeoff between QoE enhancement and network overhead: AI approaches
• AI/ML-based techniques for live streaming in 5G and 6G networks

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the “Information for Authors” section of the Paper Submission Guidelines. All manuscripts to be considered for publication must be submitted by the deadline through the magazine’s Manuscript Central submission site. Select “May2024/VideoStreaming” from the drop-down menu of Topic titles.

Important Dates

• Manuscript Submission Deadline: 31 October 2023
• Initial Decision Notification: 31 December 2023
• Revised Manuscript Due: 31 January 2024
• Final Decision Notification: 28 February 2024
• Final Manuscript Due: 15 March 2024
• Publication Date: May/June 2024

Guest Editors

• Farzad Tashtarian, Universität Klagenfurt, Austria (farzad.tashtarian@aau.at)
• Yao Liu, Rutgers University, USA (yao.liu@rutgers.edu)
• Müge Sayıt, Ege Üniversitesi, Turkey (muge.sayit@ege.edu.tr)
• Junchen Jiang, University of Chicago, USA (junchenj@uchicago.edu)
• Gwendal Simon, Synamedia, UK (gsimon@synamedia.com)
• Christian Timmerer, Universität Klagenfurt, Austria (christian.timmerer@aau.at)