IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, South Korea

http://www.vcip2023.org/

[PDF][Slides][Video]

Yiqun Liu (Ateme), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohsen Abdoli (IRT b-com),  Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Thomas Guionnet (Ateme)

Abstract: The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like VVC poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in VMAF. This improvement is observed when utilizing VVenC, an open and optimized VVC encoder implementation.

VCIP 2023: International Conference on Visual Communications and Image Processing

4 – 7 December 2023 | Jeju, korea

Conference Website

[PDF][Slides]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Prajit T Rajendran (Universite Paris-Saclay),
Samira Afzal (Alpen-Adria-Universität Klagenfurt), Klaus Schoeffmann (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: 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., HEVC, AV1) that lie below the predicted rate-distortion curve of the 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 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.

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

Community-Based QoE Enhancement for User-Generated Content Live Streaming

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

Mashad, Iran, November 1-2, 2023

[PDF][Slides]

Reza Saeedinia (University of Tehran), S. Omid Fatemi (University of Tehran),Daniele Lorenzi (Alpen-Adria Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria Universität Klagenfurt),  Christian Timmerer (Alpen-Adria Universität Klagenfurt)

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

ARTEMIS: Adaptive Bitrate Ladder Optimization for Live Video Streaming

21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 2024), APRIL 16–18, 2024, SANTA CLARA, CA, USA

[PDF][Slides]

Farzad Tashtarian (Alpen-Adria Universität Klagenfurt),  Abdelhak Bentaleb (Concordia University), Hadi Amirpour (Alpen-Adria Universität Klagenfurt) , Sergey Gorinsky (IMDEA Networks Institute),  Junchen Jiang (University of Chicago), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt) , Christian Timmerer (Alpen-Adria Universität Klagenfurt)

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 Network Special Issue on

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

Download CfP

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)