Video Coding for Efficient HTTP Adaptive Streaming

Thursday, February 8, 2024 | 09:00 am (CET) | Room: S.2.37 | | Alpen-Adria-Universität Klagenfurt

Dr. Hadi Amirpourazarian BSc.MSc. | Department of Information Technology (ATHENA Christian Doppler (CD) Laboratory)

Abstract: The widespread adoption of video streaming applications has experienced a continual upsurge, necessitating advancements in underlying technologies to ensure a seamless user experience. Central to the realm of video streaming is video coding, a key element in the efficient transmission of multimedia content.

In the context of HTTP Adaptive Streaming (HAS), videos are encoded at multiple bitrate-resolution pairs, collectively forming what is known as the bitrate ladder. This approach allows users to adapt to varying network conditions and select the most appropriate bitrate-resolution pair for a given content, thereby enhancing the overall Quality of Experience (QoE). This research extends its focus beyond (i) enhancing individual bitrate-resolution pairs to also include (ii) optimizing the construction of the entire bitrate ladder. For the former, innovative solutions utilizing content-aware deep neural networks are proposed. The primary objective is to elevate the quality of single bitrate-resolution pairs through the application of advanced deep learning techniques tailored to the unique characteristics of the content.

In pursuit of the latter objective, solutions are sought to determine the number of bitrate-resolution pairs in the bitrate ladder and their corresponding encoding parameters, such as optimal bitrate and resolution. (i) Network-assisted or (ii) content-aware approaches may be employed to find the optimal number of bitrate-resolution pairs, while content-aware solutions can assist in determining optimal resolutions, frame rates, and other parameters for the selected bitrates. Attention is also given to online bitrate ladder constructions, addressing the latency challenges inherent in such scenarios.

Recognizing that video coding is incomplete without proper assessment of quality, efforts are directed towards integrating video quality and coding. Real-time quality metrics are identified, and key parameters influencing Quality of Experience (QoE) are considered. Additionally, solutions are explored in video coding and transcoding to reduce encoding time/costs and energy consumption while minimizing quality degradation. This holistic approach aims to contribute comprehensively to the enhancement of video streaming technologies.

Bio: Hadi Amirpour is a postdoctoral research fellow at the Christian Doppler (CD) Laboratory ATHENA, based at the University of Klagenfurt. He earned his Ph.D. in computer science from the University of Klagenfurt in 2022. He holds two B.Sc. degrees in Electrical and Biomedical Engineering, as well as an M.Sc. degree in Electrical Engineering from K. N. Toosi University of Technology. Previously, he was part of the EmergIMG project, a Portuguese consortium focusing on emerging imaging technologies, funded by the Portuguese funding agency and H2020. His research interests encompass video streaming, image and video compression, quality of experience, emerging 3D imaging technology, and medical image analysis. Hadi has actively participated in standardization committees such as JPEG Pleno and MPEG. Currently, he serves as the co-chair of Qualinet TF7 since 2021.

Additionally, he has authored more than 80 publications and patents, including contributions to high-prestige journals such as IEEE  COMST, IEEE TIP, IEEE TMM, and IEEE TCSVT. He has also contributed to the organization of special sessions, workshops, etc., at international conferences, including ACM Multimedia 2022, IEEE EUVIP 2022, ACM MobiSys 2022, IEEE ICME 2023. Furthermore, he has contributed to the academic community by giving two tutorials at IEEE ICME 2023 and IEEE VCIP 2023.