On Tuesday the 25th of January 2022, Hadi Amirpour successfully defended his Ph.D. thesis under supervision of Assoc.-Prof. DI Dr. Christian Timmerer and Assoc.-Prof. Dr. Klaus Schöffmann. The defense was chaired by Assoc.-Prof. DI Dr. Mathias Lux and the examiners were Emeritus Prof. Dr. Mohammad Ghanbari (University of Essex, UK) and Univ.-Prof. DI Dr. Hermann Hellwagner (University of Klagenfurt).
We are pleased to congratulate Dr. Hadi Amirpour on passing his Ph.D. exam!
May 22-27, 2022 | Singapore
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:
Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find an optimal bitrate ladder for each video content in Video on Demand (VoD) applications. However, in live streaming applications, a fixed resolution-bitrate ladder is used to avoid the additional encoding time complexity to find optimum resolution-bitrate pairs for every video content. This paper introduces an online per-title encoding scheme (OPTE) for live video streaming applications. In this scheme, each target bitrate’s optimal resolution is predicted from any pre-defined set of resolutions using Discrete Cosine Transform(DCT)-energy-based low-complexity spatial and temporal features for each video segment. Experimental results show that, on average, OPTE yields bitrate savings of 20.45% and 28.45% to maintain the same PSNR and VMAF, respectively, compared to a fixed bitrate ladder scheme (as adopted in current live streaming deployments) without any noticeable additional latency in streaming.
Keywords:
Per-title encoding, live streaming, bitrate ladder, convex-hull prediction
IEEE Transactions on Multimedia
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Farzad Tashtarian (AAU, Austria), Abdelhak Bentaleb (NationalUniversity of Singapore), Alireza Erfanian (AAU, Austria), Hermann Hellwagner (AAU, Austria), Christian Timmerer (AAU, Austria), and Roger Zimmermann (National University of Singapore).
Abstract: While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers’ locations.
The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.
Index Terms—Live streaming, HAS, DASH, HLS, CMAF, edge computing, low latency, caching, prefetching, transcoding.
IEEE International Conference on Communications (ICC)
May 16–20, 2022 | Seoul, South Korea
Reza Farahani (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (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 emerging demands of high-definition and low-latency video streams, HTTP Adaptive Streaming (HAS) is considered the principal video delivery technology over the Internet. Network-assisted video streaming schemes, which employ modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, have been introduced as promising complementary solutions in the HAS context to improve users’ Quality of Experience (QoE) as well as network utilization. However, the existing network-assisted HAS schemes have not fully used edge collaboration techniques and SDN capabilities for achieving the aforementioned aims. To bridge this gap, this paper introduces a coLlaborative Edge- and SDN-Assisted framework for HTTP aDaptive vidEo stReaming (LEADER). In LEADER, the SDN controller collects various information items and runs a central optimization model that minimizes the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints. Due to the NP-completeness and impractical overheads of the central optimization model, we propose an online distributed lightweight heuristic approach consisting of two phases that runs over the SDN controller and edge servers, respectively. We implement the proposed framework, conduct our experiments on a large-scale testbed including 250 HAS players, and compare its effectiveness with other strategies. The experimental results demonstrate that LEADER outperforms baseline schemes in terms of both users’ QoE and network utilization, by at least 22% and 13%, respectively.
Keywords:
Dynamic Adaptive Streaming over HTTP (DASH), Network-Assisted Video Streaming, Video Transcoding, Quality of Experience (QoE), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Edge Computing, Edge Collaboration.
March 22-25, 2022 | Snowbird, Utah, US
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:
High Framerate (HFR) video streaming enhances the viewing experience and improves visual clarity. However, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, this paper proposes a content-aware frame dropping algorithm (CODA) to drop frames uniformly in every video (segment) according to the target bitrate and the video characteristics. The algorithm uses Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features to determine the video properties and then predict the optimized framerate, yielding the highest compression efficiency. The effectiveness of CODA is evaluated with High Efficiency Video Coding (HEVC) bitstreams based on the x265 HEVC open-source encoder. Experimental results show that, on average, CODA reduces the overall Ultra High Definition (UHD) encoding time by 21.82% with bit-rate savings of 15.87% and 18.20% to maintain the same PSNR and VMAF scores, respectively compared to the original frame-rate encoding.
Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.
The link to the presentation can be found here (pdf).
IEEE Global Communications Conference
December 07-11, 2021 | Madrid, Spain
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Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7 x.
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