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Reza Farahani to give a talk at 5G/6G Innovation Center, University of Surrey, UK

Collaborative Edge-Assisted Systems for HTTP Adaptive Video Streaming

5G/6G Innovation Center,  University of Surrey, UK

6th January 2023 | Guildford, UK

Abstract: The proliferation of novel video streaming technologies, advancement of networking paradigms, and steadily increasing numbers of users who prefer to watch video content over the Internet rather than using classical TV have made video the predominant traffic on the Internet. However, designing cost-effective, scalable, and flexible architectures that support low-latency and high-quality video streaming is still a challenge for both over-the-top (OTT) and ISP companies. In this talk, we first introduce the principles of video streaming and the existing challenges. We then review several 5G/6G networking paradigms and explain how we can leverage networking technologies to form collaborative network-assisted video streaming systems for improving users’ quality of experience (QoE) and network utilization.

 

 

Reza Farahani is a last-year Ph.D. candidate at the University of Klagenfurt, Austria, and a Ph.D. visitor at the University of Surrey, Uk. He received his B.Sc. in 2014 and M.Sc. in 2019 from the university of Isfahan, IRAN, and the university of Tehran, IRAN, respectively. Currently, he is working on the ATHENA project in cooperation with its industry partner Bitmovin. His research is focused on designing modern network-assisted video streaming solutions (via SDN, NFV, MEC, SFC, and P2P paradigms), multimedia Communication, computing continuum challenges, and parallel and distributed systems. He also worked in different roles in the computer networks field, e.g., network administrator, ISP customer support engineer, Cisco network engineer, network protocol designer, network programmer, and Cisco instructor (R&S, SP).

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Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

Journal: Sensors

Authors: Akif Quddus Khan, Nikolay Nikolov, Mihhail Matskin,Radu Prodan, Dumitru Roman, Bekir Sahin, Christoph Bussler, Ahmet Soylu

Abstract: Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

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Hermann Hellwagner receives Carinthian cultural prize

Every year, Carinthia celebrates its cultural and scientific greats by awarding a total of 13 prizes based on the proposal of the Carinthian Cultural Board. This year Hermann Hellwagner received one of the three appreciation prizes in the natural and technical sciences category. Congratulations! Further information: https://www.aau.at/blog/kulturpreise-des-landes-kaernten-fuer-hermann-hellwagner-roswitha-rissner-und-wolfgang-puschnig/

Paper accepted: LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming

IEEE/IFIP Network Operations and Management Symposium (NOMS)

8-12 May 2023- Miami, FL – USA

Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), Abdelhak Bentaleb (Concordia University, Canada), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), Babak Taraghi (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria), Roger Zimmermann (National University of Singapore, Singapore)

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing playback devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate adder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE

Paper accepted: CD-LwTE: Cost- and Delay-aware Light-weight Transcoding at the Edge

IEEE Transactions on Network and Service Management (TNSM)

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

Abstract—The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge’s capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose CD-LwTE, a Cost- and Delay-aware Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of CD-LwTE is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Video-on-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (i) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, CD-LwTE stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of CD-LwTE. We investigate the performance of CD-LwTE in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (i) reduces the transcoding time by up to 97%, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75%, and (iii) reduces delay by up to 48% compared to state-of-the-art approaches.

 

After the Game Jam is before the Game Jam

From December 9 to December 11, the 6th Klagenfurt Winter Jam took place at the Alpen-Adria Universität Klagenfurt. More than 80 highly motivated game enthusiasts worked for 48 hours on 21 new games and presented their results on Sunday to the public. More jammers joined online to participate remotely. It was an excellent comeback from the time of quarantines and restrictions, and the game jammers appreciated the event to make new contacts, work together, and meet old friends in a chilled and creative environment. Check out our video.

Save the date for the next Game Jams!

2nd Hüttenjam, a special event with limited seats, 13 – 16 April 2023

10th Game Jam will be on the weekend of 2 – 4 June 2023

 

 

 

Video Game Cultures 2023 – Call for Papers Out Now

We are happy to announce that the Call for Papers for our conference Video Game Cultures 2023: Exploring New Horizons is online now.

Please see our website for more info and submission.

Hadi
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Special Session on “Optimal Media Delivery” at ICME’23

ICME`23 July, 2023, Brisbane, Australia

Organizers:

  • Hadi Amirpour, University of Klagenfurt

  • Angeliki Katsenou, Trinity College Dublin, IE and University of Bristol, UK

Abstracts

Video streaming in the context of HTTP Adaptive Streaming (HAS) is replacing legacy media platforms and its market share is growing rapidly due to its simplicity, reliability, and standard support (e.g., MPEG-DASH). It results in an increasing number of video content, where nowadays, video accounts for the vast majority of today’s internet traffic either in the form of user-generated content (UGC) or pristine cinematic content. For HAS, the video is usually encoded in multiple versions (i.e., representations) of different resolutions, bitrates, codecs, etc. and each representation is divided into chunks (i.e., segments) of equal length (e.g., 2-10 second) to enable dynamic, adaptive switching during streaming based on the user’s context conditions (e.g., network conditions, device characteristics, user preferences). Read more

Hadi

IEEE TCSVT: DeepStream: Video Streaming Enhancements using Compressed Deep Neural Networks

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Journal Website

Abstract: In HTTP Adaptive Streaming (HAS), each video is divided into smaller segments, and each segment is encoded at multiple pre-defined bitrates to construct a bitrate ladder. To optimize bitrate ladders, per-title encoding approaches encode each segment at various bitrates and resolutions to determine the convex hull. From the convex hull, an optimized bitrate ladder is constructed, resulting in an increased Quality of Experience (QoE) for end-users. With the ever-increasing efficiency of deep learning-based video enhancement approaches, they are more and more employed at the client-side to increase the QoE, specifically when GPU capabilities are available. Therefore, scalable approaches are needed to support end-user devices with both CPU and GPU capabilities (denoted as CPU-only and GPU-available end-users, respectively) as a new dimension of a bitrate ladder. Read more

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Success story @ Game Studies and Engineering

Christina Obmann, one of our first Game Studies and Engineering students, has been recognized as outstanding in the Carinthia region by the local newspaper Kleine Zeitung. Besides her interest and work in games, she’s teaching at the university, learning Chinese, and was awarded a scholarship from Huawei.