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.

 

 

Josef Hammer nominated for the teaching award 2022!

Students at Klagenfurt University decide who is the best teacher: They nominate courses for the “Teaching Award 2022”. The 14 best-rated teachers submitted teaching concepts, which were evaluated and ranked by a jury. Josef Hammer was nominated this year. Congrats!

MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

conference website: IEEE NCA 2022

Samira Afzal (Alpen-Adria-Universität Klagenfurt), Zahra Najafabadi Samani (Alpen-Adria-Universität Klagenfurt), Narges Mehran (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Radu Prodan (Alpen-Adria-Universität Klagenfurt)

Abstract:

Video streaming is the dominating traffic in today’s data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost.
In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances.
We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%-50% lower completion time and 5-60% reduced total cost.

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Radu Prodan participated in the panel on “Fueling Industrial AI with Data Pipelines” at presented the Graph-Massivizer project at the European Big Data Value Forum

Radu Prodan participated in the panel on “Fueling Industrial AI with Data Pipelines” at presented the Graph-Massivizer project at the European Big Data Value Forum on November 22 in Prague, Czech Republic.