HTTP Adaptive Streaming – Quo Vadis?

Christian Timmerer, Tuesday, June 29, 2021

https://pcs2021.org/

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.

IEEE International Conference on Image Processing (ICIP)

September 19-22, 2021, Alaska, USA

[PDF] [Video] [Poster]

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:

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

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

IEEE Open Journal of Signal Processing

https://signalprocessingsociety.org/publications-resources/ieee-open-journal-signal-processing

[PDF]

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), 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

Students in MobiSys (SMS)

In Conjugation with MobiSys 2021

June 26, 2021, Mars, Solar System, Milky Way

Workshop Chairs:

• Hannaneh Barahouei Pasandi, Virginia Commonwealth University
• Mallesham Dasari, Stony Brook University
• Hadi Amirpour, University of Klagenfurt

Students Workshop in MobiSys (SMS): The SMS workshop provides a unique venue for graduate students around the world to discuss research ideas for mobile and wireless systems. The workshop is organized by a student-run TPC. SMS workshop aims to foster early-career development among students, expose them to the workings of academic life, and encourage student leadership and participation in the research community. SMS 2021 will be held in conjunction with MobiSys 2021 .

SMS Workshop provides a unique venue for graduate students around the world to present, discuss, and exchange ideas on cross-cutting research on mobile wireless networks. As its name suggests, the workshop is organized by students, and the technical sessions are given by student presenters. Submissions must be first-authored by a student. The workshop aims at fostering early-career development among students and exposing them to the workings of academic life. It provides a venue for students to learn about each others’ work and discover opportunities for collaboration.

Topics of interest include, but are not limited to:

• Experience with mobile applications, networks, and systems
• Innovative mobile, mobile sensing, and mobile crowdsourcing applications
• Tools for building and measuring mobile systems
• Innovative wearable or mobile devices
• Novel software architectures for mobile devices and mobile computing
• Data management for mobile applications
• Infrastructure support for mobile computing
• System-level energy management for mobile devices
• Operating systems for mobile devices
• Support for mobile social networking and the mobile web
• Security and privacy in mobile systems
• Resource-efficient machine learning and AI for mobile devices
• Systems for location and context sensing and awareness
• Mobile computing support for pervasive computing Vehicular and mobile robotic systems
• Systems and networking support for virtual or augmented reality
• Applications of mobile systems in health, sustainability, and smart cities
• Satellites Communication and networks
• Architectures, protocols, and algorithms in mobile network
• Measurements of mobile and network ecosystems
• Mobile data science & analysis
• Sensing using mobile phones, wearables, robots, quad-copters, crowd-sourcing etc
• Operating system and middle-ware support for mobile computing and networking
• Modeling, measurement and simulation of mobile networks
• Applications of machine learning to mobile/wireless research
• Mobile web, video, AR/VR/Immersive reality, and other applications
• User interfaces, experience, and usability for mobile applications and systems
• Data management for mobile and wireless systems

The workshop invites students to submit papers, posters, and demos through the following hotcrp link: https://sms2021.hotcrp.com/ . All submissions will be peer-reviewed by the student TPC. We encourage students with a paper, poster, or demo at ACM MobiSys main conference to present their work at SMS as well. Please contact the TPC co-chairs at  sms.mobisys@gmail.com  for any queries.

Important Dates

• Submission Deadline: June 04, 2021
• Acceptance Notifications: June 08, 2021
• Camera Ready Deadline: June 11, 2021

Efficient Multi-Encoding Algorithms for HTTP Adaptive Bitrate Streaming

Picture Coding Symposium (PCS)

29 June-2 July 2021, Bristol, UK

[PDF][Slides][Video]

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

Abstract:

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

NOSSDAV’21: The 31st edition of the Workshop on Network and Operating System Support for Digital Audio and Video

Sept. 28-Oct. 1, 2021, Istanbul, Turkey

 [PDF][Slides][Video]

Reza Farahani (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Alireza Erfanian (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: Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV)

IEEE International Conference on Multimedia and Expo (ICME)

5-9 July 2021, Shenzhen, China

[PDF][Slides][Video]

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

Abstract:

Keywords: Bitrate ladder, per-title encoding, framerate, spatial resolution.