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Paper Accepted: Efficient Transparent Access to 5G Edge Services

1st International Workshop on Edge Network Softwarization (ENS 2022) co-located with IEEE International Conference on Network Softwarization (NetSoft 2022)  Milan, Italy

Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt

Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. In [1] we argued that edge computing should be transparent to clients and introduced a solution to that end. This paper presents how to efficiently implement such a transparent approach, leveraging Software-Defined Networking. For high performance and scalability, our architecture focuses on three aspects: (i) a modular architecture that can easily be distributed onto multiple switches/controllers, (ii) multiple filter stages to avoid screening traffic not intended for the edge, and (iii) several strategies to keep the number of flows low to make the best use of the precious flow table memory in hardware switches. A performance evaluation is shown, with results from a real edge/fog testbed.

Keywords: 5G, Multi-Access Edge Computing, MEC, Patricia Trie, SDN, Software-Defined Networking

Hadi
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Paper accepted: Video Complexity Dataset at the 13th ACM Multimedia Systems Conference (ODS) track

Title: Video Complexity Dataset (VCD)

The 13th ACM Multimedia Systems Conference (ACM MMSys 2022) Open Dataset and Software (ODS) track

June 14–17, 2022 |  Athlone, Ireland

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Samira Afzal (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: This paper provides an overview of the open Video Complexity Dataset (VCD) which comprises 500 Ultra High Definition (UHD) resolution test video sequences. These sequences are provided at 24 frames per second (fps) and stored online in losslessly encoded 8-bit 4:2:0 format. In this paper, all sequences are characterized by spatial and temporal complexities, rate-distortion complexity, and encoding complexity with the x264 AVC/H.264 and x265 HEVC/H.265 video encoders. The dataset is tailor-made for cutting-edge multimedia applications such as video streaming, two-pass encoding, per-title encoding, scene-cut detection, etc. Evaluations show that the dataset includes diversity in video complexities. Hence, using this dataset is recommended for training and testing video coding applications. All data have been made publicly available as part of the dataset, which can be used for various applications.
The details of VCD can be accessed online at https://vcd.itec.aau.at.

Farzad Tashtarian to give a short talk at CHIST-ERA conference

Optimizing QoE in Live Streaming over Wireless Networks using Machine Learning Techniques

May 25, 2022, Edinburgh, UK

Empowered by today’s rich tools for media generation and collaborative production and the convenient wireless access (e.g., WiFi and cellular networks) to the Internet, crowdsourced live streaming over wireless networks have become very popular. However, crowdsourced wireless live streaming presents unique video delivery challenges that make a difficult tradeoff among three core factors: bandwidth, computation/storage, and latency. However, [read more]

 

 

 

 

 

Paper accepted – CoPaM – Elsevier Computer Communications journal

CoPaM: Cost-aware VM Placement and Migration for Mobile services in Multi-Cloudlet environment: An SDN-based approach

Elsevier Computer Communications journal 

 

Shirzad Shahryari (Ferdowsi University of Mashhad, Mashhad, Iran), Farzad Tashtarian  (Alpen-Adria-Universität Klagenfurt), Seyed-Amin Hosseini-Seno (Ferdowsi University of Mashhad, Mashhad, Iran).

Abstract: Edge Cloud Computing (ECC) is a new approach for bringing Mobile Cloud Computing (MCC) services closer to mobile users in order to facilitate the complicated application execution on resource-constrained mobile devices. The main objective of the ECC solution with the cloudlet approach is mitigating the latency and augmenting the available bandwidth. This is basically done by deploying servers (a.k.a ”cloudlets”) close to the user’s device on the edge of the cellular network. Once the user requests mount, the resource constraints in a cloudlet will lead to resource shortages. This challenge, however, can be overcome using a network of cloudlets for sharing their resources. On the other hand, when considering the users’ mobility along with the limited resource of the cloudlets serving them, the user-cloudlet communication may need to go through multiple hops, which may seriously affect the communication delay between them and the quality of services (QoS).


Hadi
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EUVIP 2022 Special Session

EUVIP 2022 Special Session on

“Machine Learning for Immersive Content Processing”

September, 2022, Lisbon, Portugal

Link

Organizers:

  • Hadi Amirpour, Klagenfurt University, Austria
  • Christine Guillemot, INSA, France
  • Christian Timmerer, Klagenfurt University, Austria

 

Brief description:

The importance of remote communication is becoming more and more important in particular after  COVID-19 crisis. However, to bring a more realistic visual experience, more than the traditional two-dimensional (2D) interfaces we know today is required. Immersive media such as 360-degree, light fields, point cloud, ultra-high-definition, high dynamic range, etc. can fill this gap. These modalities, however, face several challenges from capture to display. Learning-based solutions show great promise and significant performance in improving traditional solutions in addressing the challenges. In this special session, we will focus on research works aimed at extending and improving the use of learning-based architectures for immersive imaging technologies.

Important dates:

Paper Submissions: 6th June, 2022
Paper Notifications: 11th July, 2022

 

Vignesh V Menon

Paper accepted – OPSE: Online Per-Scene Encoding for Adaptive HTTP Live Streaming

2022 IEEE International Conference on Multimedia and Expo (ICME) Industry & Application Track

July 18-22, 2022 | Taipei, Taiwan

Conference Website

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

Abstract:

In live streaming applications, typically a fixed set of bitrate-resolution pairs (known as a bitrate ladder) is used during the entire streaming session in order to avoid the additional latency to find scene transitions and optimized bitrate-resolution pairs for every video content. However, an optimized bitrate ladder per scene may result in (i) decreased
storage or delivery costs or/and (ii) increased Quality of Experience (QoE). This paper introduces an Online Per-Scene Encoding (OPSE) scheme for adaptive HTTP live streaming applications. In this scheme, scene transitions and optimized bitrate-resolution pairs for every scene are predicted using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features. Experimental results show that, on average, OPSE yields bitrate savings of upto 48.88% in certain scenes to maintain the same VMAF,
compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming.

The bitrate ladder prediction envisioned using OPSE.

Vignesh V Menon
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Paper accepted: Perceptually-aware Per-title Encoding for Adaptive Video Streaming

2022 IEEE International Conference on Multimedia and Expo (ICME)

July 18-22, 2022 | Taipei, Taiwan

Conference Website

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:

In live streaming applications, typically a fixed set of bitrate-resolution pairs (known as bitrate ladder) is used for simplicity and efficiency in order to avoid the additional encoding run-time required to find optimum resolution-bitrate pairs for every video content. However, an optimized bitrate ladder may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience (QoE). This paper introduces a perceptually-aware per-title encoding (PPTE) scheme for video streaming applications. In this scheme, optimized bitrate-resolution pairs are predicted online based on Just Noticeable Difference (JND) in quality perception to avoid adding perceptually similar representations in the bitrate ladder. To this end, Discrete Cosine Transform(DCT)-energy-based low-complexity spatial and temporal features for each video segment are used. Experimental results show that, on average, PPTE yields bitrate savings of 16.47% and 27.02% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming accompanied by a 30.69% cumulative decrease in storage space for various representations.

 

Architecture of PPTE


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5G Kärntner Fog project: Kick-off meeting in Klagenfurt

The kick-off meeting of the “5G-KärntnerFog” Project took place on April, 21st, 2022 at Klagenfurt University. The purpose of this first meeting was primarily the definition of work structures, work packages, and getting to know each partner region. The project partners consist of the following institutions: ITEC (Lead), FH Kärnten, and Siplan. 

Narges Mehran presented at HiPEAC Student Challenge IX: IoT for everyone

Title: A Traffic-sign recognition IoT-based Application
Authors: Narges Mehran, Dragi Kimovski, Zahra Najafabadi Samani, Radu Prodan
The work “A Traffic-sign recognition IoT-based Application” got granted for the presentation in the HiPEAC IoT challenge during CSW Spring 2022.
International data corporation predicts that 21.5  billion connected Internet of Things (IoT) devices will generate 55% of all data by 2025. Nowadays, camera sensors can be embedded in most devices. Therefore, we designed an application to receive a video stream from a camera sensor and perform the video processing. First our designed application pre-processes the sensed data by two high-quality video encoding and framing frameworks. Afterward, we apply the machine learning  (ML) model based on the low and high training accuracies. Because the user devices cannot often perform high-load machine learning training operations, we consider the ML inference operation acting as a lightweight trained ML model. At the end, the processed data is packaged for the consumer such as the driver of a car.