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Paper accepted: ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

28th International Conference on Multimedia Modeling (MMM)

April 05-08, 2022 | Qui Nhon, Vietnam

Conference Website

Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive
bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques
to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.

Keywords: HTTP Adaptive Streaming, Edge Computing, Content Delivery, Network-assisted Video Streaming, Quality of Experience, Machine Learning.

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Paper accepted: MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks

28th International Conference on Multimedia Modeling (MMM)

April 05-08, 2022 | Qui Nhon, Vietnam

Conference Website

Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Deep neural network (DNN) based approaches have been intensively studied to improve video quality thanks to their fast advancement in recent years. These approaches are designed mainly for desktop devices due to their high computational cost. However, with the increasing performance of mobile devices in recent years, it became possible to execute DNN based approaches in mobile devices. Despite having the required computational power, utilizing DNNs to improve the video quality for mobile devices is still an active research area. In this paper, we propose an open-source mobile platform, namely MoViDNN, to evaluate DNN based video quality enhancement methods, such as super-resolution, denoising, and deblocking. Our proposed platform can be used to evaluate the DNN based approaches both objectively and subjectively. For objective evaluation, we report common metrics such as execution time, PSNR, and SSIM. For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposed platform is available publicly at https://github.com/cd-athena/MoViDNN

Keywords: Super resolution, Deblocking, Deep Neural Networks, Mobile Devices

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Paper accepted: LwTE-Live: Light-weight Transcoding at the Edge for Live Streaming

The 1st ACM CoNEXT Workshop on Design, Deployment, and Evaluation of Network-assisted  video Streaming (ViSNext 2021)

Conference Website

Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.

Keywords: live video streaming, network function virtualization, NFV, light-weight transcoding, transcoding, edge computing

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Paper accepted: ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator

The 10th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

Conference Website

Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: In this work, we present ANGELA, HTTP adaptive streaming (HAS) and Edge Computing Simulator. ANGELA was designed to test edge mechanisms that support HAS, as it offers: realistic radio layer simulation, different multimedia content configurations, access to radio and player metrics at the edge, and a wide variety of metrics to evaluate the video streaming session performance. The ANGELA architecture is flexible and can support adaptive bitrate (ABR) algorithms located at different points of the network. Moreover, we show the possibilities of Angela by evaluating different ABR algorithms.

Keywords: Network simulator, testbed, edge computing, HTTP Adaptive Streaming.

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Paper accepted: Days of Future Past: An Optimization-based Adaptive Bitrate Algorithm over HTTP/3

The ACM CoNEXT 2021 Workshop on the Evolution, Performance, and Interoperability of QUIC (EPIQ)

07 December 2021  | Munich, Germany (Online)

Workshop Website

Daniele Lorenzi (Department of Information Engineering, University of PaduaItaly), Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Simone Milani (Department of Information Engineering, University of PaduaItaly), Herman Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt),  Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: HTTP Adaptive Streaming(HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behaviour according to changing network conditions it may result in video quality variations that negatively impacts the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization-based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing, and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both next segment requests and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., re-transmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9 %.

Keywords: HTTP/3, QUIC, Days of Future Past, HAS, QoE

Negin Ghamsarian completed her PhD

On October 27th, 2021, Negin Ghamsarian successfully defended her thesis on “Deep-Learning-Assisted Analysis of Cataract Surgery VIdeos” under the supervision of Prof. Klaus Schöffmann. The defense was chaired by Prof. Hermann Hellwagner and the examiners were Prof. Henning Müller (University of Applied Sciences Western Switzerland and the University of Geneva) and Prof. Raphael Sznitman (University of Bern). Congratulations to Dr. Ghamsarian for this great achievement!

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Paper Accepted @ Blockchain for Smart Cyber-Physical Systems – UCC’2021

Title: Monitoring System Architecture for the Multi-Scale Blockchain-based Logistic Network

Authors: Vladislav Kashansky, Radu Prodan, Aso Validi, Cristina Olaverri-Monreal, Gleb Radchenko

Abstract: Contemporary control processes and methods in multi-scale, cyber-physical systems require precise data collection at various levels, timely transmission, and analysis involving large number of computing and storage elements connected within high-performance permissioned consensus networks. For example, in transport networks, resources tend to form multi-scale dynamical systems with diverse operational requirements, including data exchange policies and consensus protocols. Apart from designing complete topology, chaincodes and consensus logic, effective monitoring of the applications and infrastructure of such complex systems remains a research challenge. In this paper, we discuss important aspects of the data-intensive applications monitoring investigated in the frames of the ADAPT project.

We present highlights on the tool-sets, architectures and details on possible optimization approaches for monitoring data collection. We introduce a dynamic multi-scale monitoring system architecture with preliminary workflow model. It allows obtaining effective low-latency publish-subscribe matching of the dynamically varying monitoring tasks and executing machines.

Keywords: Logistics, transportation, decentralization, blockchain, monitoring systems, optimization, data-intensive systems, hybrid systems

Dragi Kimovski
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The final ASPIDE paper has been accepted in the “Frontiers in Big Data” Journal

Title: Autotuning of exascale applications with anomalies detection

Authors: Dragi Kimovski, Roland Mathá, Gabriel Iuhasz, Fabrizio Marozzo, Dana Petcu, Radu Prodan

Abstract: The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application run-time parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and run-time parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning of exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objectives functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.

Keywords: Exascale computing, Autotuning, Events and Anomalies Detection, Multi-objective Optimisation

 

Univ.-Prof. DI Dr. Radu Prodan for being discussion leader in licentiate seminar

Univ.-Prof. DI Dr. Radu Prodan was nominated as discussion leader for the defensio of Mrs. Zeinab Bakhshi from Mälardalen University in Stockholm Sweden.

 

 

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Farzad Tashtarian to give a talk at the University of Isfahan, Isfahan, Iran

Farzad Tashtarian is invited to talk on “Network-Assisted Video Streaming” at the University of Isfahan, Isfahan, Iran.