Multimedia Communication

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Paper accepted: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Conference info: IEEE LCN

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

Abstract: Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE).

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Throwback on our special session at PCS’21

A Special Session on ‘Video Coding for Large Scale HTTP Adaptive Streaming Deployments‘ was organized by Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), and Alex Giladi (Comcast, USA) on July 2 at the 35th Picture Coding Symposium (PCS) 2021. Read more about it here.

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Paper accepted: A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

Conference info: The 46th IEEE Conference on Local Computer Networks (LCN) October 4-7, 2021

Authors: Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Abdelhak Bentaleb (National University of Singapore), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), and Roger Zimmermann (National University of Singapore)

Abstract: Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. Read more

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Paper accepted – CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming

Conference info: The 46th IEEE Conference on Local Computer Networks (LCN) October 4-7, 2021

Authors: Reza Farahani (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. Read more

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Christian Doppler ‘pilot’ laboratory ATHENA to transition to a regular CD laboratory

Successful review of the first research phase: Christian Doppler ‘pilot’ laboratory ATHENA to transition to a regular CD laboratory two years after launch. Read more about it at ATHENA website and the press release at AAU website.

 

Hadi
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Hadi Amirpour appointed co-chair of IMEx

Hadi Amirpour has been appointed co-chair of Task Force 7 (TF7)
Immersive Media Experience (IMEx) at the 15th Qualinet meeting

Co-chairs:

 

TF7: Immersive Media Experiences (IMEx)

Immersive media applications are entering our daily lives starting from VR/AR/360° video applications to multi-sensory/multimedia experiences potentially addressing all human senses rather than focusing on hearing and seeing. The overall goal of providing Immersive Media Experiences (IMEx) to end-users is giving them the sensation of being part of the particular media which shall result in a worthwhile, informative user and quality of experience.

The actual objectives of this task force are as follows:

  • disseminating the white paper
  • working towards submission of the extended version
  • liaison with other communities (UX, sensory sciences) and standards developing organizations (JPEG, MPEG, EBU)
  • Identification of different QoE aspects of immersive experiences
  • QoE models and QoE assessment approaches for immersive experiences, addressing various audiovisual modalities; e.g. HDR, omnidirectional video, light fields, point clouds, and spatial audio.
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Christian Timmerer to give a Keynote at the 35th Picture Coding Symposium 2021

HTTP Adaptive Streaming – Quo Vadis?

Christian Timmerer, Tuesday, June 29, 2021

35th Picture Coding Symposium (PCS) 2021

Abstract: Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both.

This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry.

In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of the international multimedia systems research.

Vignesh V Menon
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Paper accepted: Efficient Content-Adaptive Feature-based Shot Detection for HTTP Adaptive Streaming

At IEEE International Conference on Image Processing (ICIP) on September 19-22, 2021, Alaska, USA.

Authors: Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract: Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of DASH. The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in  VoD HAS applications.
This paper proposes a novel DCT feature-based shot detection and successive elimination algorithm for shot detection algorithm and benchmark the algorithm against the default shot detection algorithm of the x265 implementation of the HEVC standard. Our experimental results demonstrate that the proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.

Keywords: HTTP Adaptive Streaming, Video-on-Demand, Shot detection, multi-shot encoding.

Link: https://2021.ieeeicip.org

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Paper accepted: Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning

IEEE Open Journal of Signal Processing

Authors: Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, University of Essex)

Abstract: Video streaming applications keep getting more attention over the years, and HTTP Adaptive Streaming (HAS) became the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08% and parallel encoding time by 41.26%, with a 0.89% bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27% for the overall encoding and 27.71% for the parallel encoding on average with a 2.05% bitrate
increase.

Keywords: HTTP Adaptive Streaming, HEVC, Multirate Encoding, Machine Learning

Vignesh V Menon
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Paper accepted: Efficient Multi-Encoding Algorithms for HTTP Adaptive Bitrate Streaming

Conference info: Picture Coding Symposium (PCS), 29 June-2 July 2021, Bristol, UK

Conference Website: https://pcs2021.org

Authors: Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)

Abstract: Since video accounts for the majority of today’s internet traffic, the popularity of HTTP Adaptive Streaming (HAS) is increasing steadily. In HAS, each video is encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to a heterogeneity of network conditions, device characteristics, and end-user preferences. Most of the streaming services utilize cloud-based encoding techniques which enable a fully parallel encoding process to speed up the encoding and consequently to reduce the overall time complexity. State-of-the-art approaches further improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to improve the encoding time complexity of the remaining representations. In this paper, we investigate various multi-encoding algorithms (i.e., multi-rate and multi-resolution) and propose novel multi- encoding algorithms for large-scale HTTP Adaptive Streaming deployments. Experimental results demonstrate that the proposed multi-encoding algorithm optimized for the highest compression efficiency reduces the overall encoding time by 39% with a 1.5% bitrate increase compared to stand-alone encodings. Its optimized version for the highest time savings reduces the overall encoding time by 50% with a 2.6% bitrate increase compared to stand-alone encodings.

Keywords: HTTP Adaptive Streaming, HEVC, Multi-rate Encoding, Multi-encoding.