IEEE Access

[PDF]

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

Abstract: Multi-access Edge Computing (MEC) is a new paradigm that brings storage and computing close to the clients. MEC enables the deployment of complex network-assisted mechanisms for video streaming that improve clients’ Quality of Experience (QoE). One of these mechanisms is segment prefetching, which transmits the future video segments in advance closer to the client to serve content with lower latency. In this work, for HAS-based (HTTP Adaptive Streaming) video streaming and specifically considering a cellular (e.g., 5G) network edge, we present our approach Segment Prefetching and Caching at the Edge for Adaptive Video Streaming (SPACE). We propose and analyze different segment prefetching policies that differ in resource utilization, player and radio metrics needed, and deployment complexity. This variety of policies can dynamically adapt to the network’s current conditions and the service provider’s needs. We present segment prefetching policies based on diverse approaches and techniques: past segment requests, segment transrating (i.e., reducing segment bitrate/quality), Markov prediction model, machine learning to predict future segment requests, and super-resolution.We study their performance and feasibility using metrics such as QoE characteristics, computing times, prefetching hits, and link bitrate consumption. We analyze and discuss which segment prefetching policy is better under which circumstances, as well as the influence of the client-side Adaptive Bit Rate (ABR) algorithm and the set of available representations (“bitrate ladder”) in segment prefetching. Moreover, we examine the impact on segment prefetching of different caching policies for (pre-)fetched segments, including Least Recently Used (LRU), Least Frequently Used (LFU), and our proposed popularity-based caching policy Least Popular Used (LPU).

Keywords: Adaptive video streaming, content delivery, HAS, edge computing, cellular network edge, MEC, segment prefetching, segment caching.

How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions

Abstract: Empowered by today’s rich tools for media generation and collaborative production and convenient network access to the Internet, video streaming has become very popular. Dynamic adaptive video streaming is a technique used to deliver video content to users over the Internet, where the quality of the video adapts in real time based on the network conditions and the capabilities of the user’s device. HTTP Adaptive Streaming (HAS) has become the de-facto standard to provide a smooth and uninterrupted viewing experience, especially when network conditions frequently change. Improving the QoE of users concerning various applications‘ requirements presents several challenges, such as network variability, limited resources, and device heterogeneity. For example, the available network bandwidth can vary over time, leading to frequent changes in the video quality. In addition, different users have different preferences and viewing habits, which can further complicate live streaming optimization. Researchers and engineers have developed various approaches to optimize dynamic adaptive streaming, such as QoE-driven adaptation, machine learning-based approaches, and multi-objective optimization, to address these challenges. In this talk, we will give an introduction to the topic of video streaming and point out the significant challenges in the field. We will present a layered architecture for video streaming and then discuss a selection of approaches from our research addressing these challenges. For instance, we will present approaches to improve the  QoE of clients in User-generated content applications in centralized and distributed fashions. Moreover, we will present a novel architecture for low-latency live streaming that is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches.

Fast multi-rate encoding for adaptive HTTP streaming

US Patent

[PDF]

Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetynkaya (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: According to embodiments of the disclosure, information of higher and lower quality encoded video segments is used to limit Rate-Distortion Optimization (RDO) for each Coding Unit Tree (CTU). A method first encodes the highest bit-rate segment and consequently uses it to encode the lowest bit-rate video segment. Block structure and selected reference frame of both highest and lowest bit-rate video segments are used to predict and shorten RDO process for each CTU in middle bit-rates. The method delays just one frame using parallel processing. This approach provides time-complexity reduction compared to the reference software for middle bit-rates while degradation is negligible.

Another Valentine’s day, another gift from ATHENA! We are delighted to share Video Complexity Analyzer (VCA) version 2.0. The new release brings add-on benefits over our previous release, v1.5, like supporting analysis of High Dynamic Range (HDR) videos. The version is faster, courtesy of the low-pass DCT optimizations. We expect this release of VCA facilitates research on content-adaptive encoding, video quality assessment, and other research topics more than ever.

A command-line executable is provided to facilitate testing and development. VCA is available as an open-source library published under the GPLv3 license. For more details, please visit the online software documentation here. The source code can be found here.

Heatmap depiction of the luma texture information features extracted from the second frame of Cover- Song_1080P_0a86 video of Youtube UGC Dataset.

On February 8, 2023, EduDay – organised by the educational lab and students of the HAK 1 Klagenfurt – took place for the first time. Several hundred students were guided through the laboratories and got their first insight into research. CD laboratory ATHENA participated as well and presented background and results from the world of video streaming to the interested participants.

Find more info here.

2023 NAB Broadcast Engineering and Information Technology (BEIT) Conference

April 15-19, 2023 | Las Vegas, US

[PDF]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Prajit T Rajendran (Universite Paris-Saclay), Christian Feldmann (Bitmovin, Klagenfurt), Martin Smole (Bitmovin, Klagenfurt), Klaus Schoeffmann (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:

Currently, a fixed set of bitrate-resolution pairs termed bitrate ladder is used in live streaming applications. Similarly, two-pass variable bitrate (VBR) encoding schemes are not used in live streaming applications to avoid the additional latency added by the first-pass. Bitrate ladder optimization is necessary to (i) decrease storage or delivery costs or/and (ii) increase Quality of Experience (QoE). Using two-pass VBR encoding improves compression efficiency, owing to better encoding decisions in the second-pass encoding using the first-pass analysis. In this light, this paper introduces a perceptually-aware constrained Variable Bitrate (cVBR) encoding Scheme (Live VBR) for HTTP adaptive streaming applications, which includes a joint optimization of the perceptual redundancy between the representations of the bitrate ladder, maximizing the perceptual quality (in terms of VMAF) and optimized constant rate factor (CRF). Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for every video segment, namely, brightness, spatial texture information, and temporal activity, are extracted to predict perceptually-aware bitrate ladder for encoding. Experimental results show that, on average, Live VBR yields bitrate savings of 7.21% and 13.03% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder Constant Bitrate (CBR) encoding using x264 AVC encoder without any noticeable additional latency in streaming, accompanied by a 52.59% cumulative decrease in storage space for various representations, and a 28.78% cumulative decrease in energy consumption, considering a perceptual difference of 6 VMAF points.

The encoding pipeline using Live VBR envisioned in this paper.