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Paper accepted at ACM TOMM: COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth Utilization

Authors: Michael Seufert (University of Augsburg, Germany), Marius Spangenberger (University of Würzburg, Germany), Fabian Poignée (University of Würzburg, Germany), Florian Wamser (Lucerne University of Applied Sciences and Arts, Switzerland), Werner Robitza (AVEQ GmbH, Austria), Christian Timmerer (Christian Doppler-Labor ATHENA, Alpen-Adria-Universität, Austria), Tobias Hoßfeld (University of Würzburg, Germany)

Journal: ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)

Abstract: Reaching close-to-optimal bandwidth utilization in Dynamic Adaptive Streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the Quality of Experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm MinOff, which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding (JITE), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our JITE DASH system, which can offer a continuous bit rate slide, in terms of bandwidth utilization and video QoE for different ABR algorithms.

Paper accepted: HEFTLess: A Bi-Objective Serverless Workflow Batch Orchestration on the Computing Continuum

Authors: Reza Farahani, Narges Mehran, Sashko Ristov, and Radu Prodan

Venue: IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan, 24-27 September

Abstract: Extending cloud computing towards fog and edge computing yields a heterogeneous computing environment known as computing continuum. In recent years, increasing demands for scalable, cost-effective, and streamlined maintenance services have led application and service providers to prefer serverless models over monolithic and serverful processing. However, orchestrating the computing continuum in complex application workflows of serverless functions, each with distinct requirements, introduces new resource management and scheduling
challenges. This paper introduces an orchestration service for concurrent serverless workflow processing across the computing continuum called HEFTLess. HEFTLess uses two deployment modes tailored to serve each workflow function: predeployed and undeployed. We formulate the problem as a Binary Linear Programming (BLP) optimization model, incorporating multiple groups of constraints to minimize the overall completion time and monetary cost of executing workflow batches. Inspired by the Heterogeneous Earliest Finish Time (HEFT) algorithm, we
propose a lightweight serverless workflow scheduling heuristic to cope with the high optimization time complexity in polynomial time. We evaluate HEFTLess using two machine learning-based serverless workflows on a real computing continuum testbed, including AWS Lambda and 325 combined on-promise and cloud instances from Exoscale, distributed across five geographic locations. The experimental results confirm that HEFTLess outperforms state-of-the-art methods in terms of both workflow batch completion time and cost.

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Journal article accepted: The Computing Continuum: From IoT to the Cloud

Autohors: Auday Al-Dulaimy, Matthijs Jansen, Bjarne Johansson, Animesh Trivedi, Alexandru Iosup, Mohammad Ashjaei, Antonino Galletta, Dragi Kimovski, Radu Prodan, Konstantinos Tserpes, George Kousiouris, Chris Giannakos, Ivona Brandic, Nawfal Ali, Andre B. Bondi, Alessandro V. Papadopoulos

Journal “Internet of things”: https://link.springer.com/journal/43926

Abstract:

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum.

The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge-cloud computing models and the other for edge-cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

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DataCloud (EU project) final review was a great success!

The review of the DataCloud project (Radu & his team were involved as partners; the project was funded by the EU) took place on 25.06.204 – the final review was a complete success, showcasing the outstanding results achieved.

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Klaus Schöffmann gave a Keynote Talk about “The Use of AI in Image and Video Retrieval Over the last Decade” at the MVRMLM 2024 Workshop, co-located with ACM ICMR 2024

Together with Cathal Gurrin from DCU, Ireland, on June 14, 2024, Klaus Schöffmann gave a keynote talk about “From Concepts to Embeddings. Charting the Use of AI in Digital Video and Lifelog Search Over the Last Decade” at the International Workshop on Multimodal Video Retrieval and Multimodal Language Modelling (MVRMLM’24), co-located with the ACM ICMR 2024 conference in Phuket, Thailand.

Link: https://mvrmlm2024.ecit.qub.ac.uk

Here is the abstract of the talk:

In the past decade, the field of interactive multimedia retrieval has undergone a transformative evolution driven by the advances in artificial intelligence (AI). This keynote talk will explore the journey from early concept-based retrieval systems to the sophisticated embedding-based techniques that dominate the landscape today. By examining the progression of such AI-driven approaches at both the VBS (Video Browser Showdown) and the LSC (Lifelog Search Challenge), we will highlight the pivotal role of comparative benchmarking in accelerating innovation and establishing performance standards. We will also forward at the potential future developments in interactive multimedia retrieval benchmarking, including emerging trends, the integration of multimodal data, and the future comparative benchmarking challenges within our community.

 

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ITEC Wins Lifelog Search Challenge 2024 (LSC 2024) Again!

On June 10, 2024, the 7th Lifelog Search Challenge (LSC 2024), an international competition on lifelog retrieval took place as a workshop at the ACM International Conference on Multimedia Retrieval (ICMR 2024) in Phuket, Thailand. The LSC is organized by a large international team (Cathal Gurrin, Björn Þór Jónsson, Duc-Tien Dang-Nguyen, Jakub Lokoc, Klaus Schoeffmann, Minh-Triet Tran, Steve Hodges, Graham Healy, Luca Rossetto, and Werner Bailer) and attracted 21 teams from all around the world (Austria, Czechia, Germany, Iceland, Ireland, Italy, Netherlands, Norway, Portugal, Switzerland, and Vietnam). The competition tests how fast and accurate state-of-the-art lifelog retrieval systems can solve search tasks (known-item search, ad-hoc search, visual question answering) in a shared dataset of about 720000 images, collected by an anonymous  lifelogger over 18 months. With the LIFEXPLORE system developed by Martin Rader, Mario Leopold, and Klaus Schöffmann, ITEC could win this competition for the second time in a row and was awarded for the Best LSC System. Congratulations!

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ACM ICMR 2024 Successfully Concluded, co-organized by ITEC

From June 10, 2024 until June 14, 2024, the ACM International Conference on Multimedia Retrieval (ICMR 2024) took place in Phuket, Thailand. It was organized by Cathal Gurrin (DCU), Klaus Schoeffmann (ITEC, AAU), and Rachada Kongkachandra (Thammasat University). ICMR 2024 received 348 paper submissions and about 80 more to the nine co-located workshops (LSC’24, AI-SIPM’24, MORE’24, ICDAR’24, MAD’24, AIQAM’24, MUWS’24, R2B’24, and MVRMLM’24). The conference attracted about 202 on-site participants (including local organizers), with 10 oral sessions, an on-site and a virtual poster session, a demo session, a reproducibility session, two interesting keynotes about Multimodal Retrieval in Computer Vision (Mubarak Shah) and AI-Based Video Analytics (Supavadee Aramvith), a panel about LLM and Multimedia (Alan Smeaton), and four interesting tutorials.

Link: www.icmr2024.org

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Hadi Amirpour received Best Paper Award @ 2024 Picture Coding Symposium

At the PCS 2024 (Picture Coding Symposium), held in Taichung, Taiwan from June 12-14, Hadi Amirpour received the Best Paper Award for the paper “Beyond Curves and Thresholds – Introducing Uncertainty Estimation To Satisfied User Ratios for Compressed Video” written together with Jingwen Zhu, Raimund Schatz, Patrick Le Callet and Christian Timmerer. Congratulations!

Interview with Felix Schniz on the occasion of the 40th birthday of the video game classic “Tetris”

To celebrate the 40th birthday of a video game classic, Lukas Lorber from Kleine Zeitung interviewed Felix Schniz about Tetris. The interview touches upon the Cold War history of the video game, the psychology behind the ‘Tetris Effect’, and various annotations by genre expert Felix Schniz about the secret behind the game’s ongoing success.

You can read the full interview here: https://www.kleinezeitung.at/wirtschaft/gaming/18530006/40-jahre-tetris-aus-dem-kalten-krieg-in-die-unsterblichkeit.

 

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Paper accepted: Video Encoding Enhancement via Content-Aware Spatial and Temporal Super-Resolution

Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria),  Ahmed Telili (INSA Rennes, France), Wassim Hamidouche (INSA Rennes, France), Guo Lu (Shanghai Jiao Tong University, China) and Christian Timmerer (AAU, Austria)

Venue: European Signal Processing Conference (EUSIPCO)

Abstract: Content-aware deep neural networks (DNNs) are trending in Internet video delivery. They enhance quality within bandwidth limits by transmitting videos as low-resolution (LR) bitstreams with overfitted super-resolution (SR) model streams to reconstruct high-resolution (HR) video on the decoder end. However, these methods underutilize spatial and temporal redundancy, compromising compression efficiency. In response, our proposed video compression framework introduces spatial-temporal video super-resolution (STVSR), which encodes videos into low spatial-temporal resolution (LSTR) content and a model stream, leveraging the combined spatial and temporal reconstruction capabilities of DNNs. Compared to the state-of-the-art approaches that consider only spatial SR, our approach achieves bitrate savings of 18.71% and 17.04% while maintaining the same PSNR and VMAF, respectively.