ATHENA Christian Doppler (CD) Laboratory

VCIP 2023
Monday, Dec4, 2023
https://vcip2023.iforum.biz/page/goto

Lectures:

• Hadi Amirpour, AAU, AT
• Christian Timmerer, AAU, AT

Abstract: The applications of video streaming are the primary drivers of Internet traffic, as over 82% of IP traffic in 2022. HTTP Adaptive Streaming (HAS) is the prevailing technology to stream live and video-on-demand (VoD) content over the Internet. In HAS, firstly, the video is split into smaller chunks, referred to as segments, each characterized by a short playback time interval. Segments are then encoded at multiple representations (bitrate-resolution pairs), referred to as the bitrate ladder. The clients select the highest possible quality representation for the next segment, which adapts to the current network condition and device type. In this way, HAS enables the continuous delivery of live and VoD content over the Internet. As the demand for video streaming applications is on the rise, new generations of video codecs and video content optimization algorithms are being developed. This tutorial first presents a detailed overview of video codecs, in particular the most recent standard video codec, i.e., Versatile Video Coding (VVC). On top of the video codecs, per-title encoding methods, which optimize bitrate ladders over characteristics of videos, are then introduced. It will be presented how representations are selected in a way that bitrate ladders are optimized over various dimensions, including spatial resolution, frame rate, energy consumption, device type, and network condition. Next, novel methods that optimize bitrate ladders for live video streaming without adding noticeable latency are introduced. Finally, fast multi-rate encoding approaches that reduce the encoding time and cost are introduced.

At Christian Doppler laboratory ATHENA, we offer an internship*)**) for 2024 for Master Students, and we kindly request your applications by the 19th of January 2024 with the following data (in German or English):

• CV
• Record of study/transcript (“Studienerfolgsnachweis”)

*) A 3 months period in 2024 (with an exact time slot to be discussed) with the possibility to spend up to 1-month at the industrial partner; 20h per week “Universitäts-KV, Verwendungsgruppe C1, studentische Hilfskraft”
**) Depending on whether the funding gets approval from the CDG.

Please send your application by email to christian.timmerer@aau.at.

About ATHENA: The Christian Doppler laboratory ATHENA (AdapTive Streaming over HTTP and Emerging Networked MultimediA Services) is jointly proposed by the Institute of Information Technology (ITEC; http://itec.aau.at) at Alpen-Adria-Universität Klagenfurt (AAU) and Bitmovin GmbH (https://bitmovin.com) to address current and future research and deployment challenges of HAS and emerging streaming methods. AAU (ITEC) has been working on adaptive video streaming for more than a decade, has a proven record of successful research projects and publications in the field, and has been actively contributing to MPEG standardization for many years, including MPEG-DASH; Bitmovin is a video streaming software company founded by ITEC researchers in 2013 and has developed highly successful, global R&D and sales activities and a world-wide customer base since then.

The aim of ATHENA is to research and develop novel paradigms, approaches, (prototype) tools, and evaluation results for the phases

1. multimedia content provisioning,
2. content delivery, and
3. content consumption in the media delivery chain as well as for
4. end-to-end aspects, with a focus on, but not being limited to, HTTP Adaptive Streaming (HAS).

The new approaches and insights are to enable Bitmovin to build innovative applications and services to account for the steadily increasing and changing multimedia traffic on the Internet.

Klagenfurt, August 22, 2023

Congratulations to Dr. Reza Farahani for successfully defending his dissertation on “Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA.

Abstract

Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low- latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client- based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.

1. Our first contribution concentrates on leveraging the capabilities of the Software Defined Networking (SDN), Network Function Virtualization (NFV), and Multi-Access Edge Comput- ing (MEC) paradigms to introduce ES-HAS and CSDN as edge- and SDN-assisted frameworks, mainly for VoD and live streaming, respectively. ES-HAS and CSDN introduce Virtual Network Functions (VNFs) named Virtual Reverse Proxy (VRP) servers at the edge of an SDN-enabled network to collect HAS clients’ requests and retrieve networking information. The SDN controller in these systems manages a single domain network. VRP servers perform optimiza- tion models as server/segment selection policies to serve clients’ requests with the shortest fetching time by selecting the most appropriate cache server/video segment quality or by reconstructing the requested quality through transcoding at the edge. Deployment of ES-HAS and CSDN on the cloud-based testbeds and estimation of users’ Quality of Experience (QoE) using objective metrics demonstrates how clients’ requests can be served with higher QoE (by 40%) and lower bandwidth usage (by 63%) compared to state-of-the-art approaches.
2. Our second contribution designs an architecture that simultaneously supports various types of video streaming (live and VoD), considering their versatile QoE and latency require- ments. To this end, the SDN, NFV, and MEC paradigms are leveraged, and three VNFs, i.e., Virtual Proxy Function (VPF), Virtual Cache Function (VCF), and Virtual Transcoding Function (VTF), are designed. We build a series of these function chains through the Service Function Chaining (SFC) paradigm, utilize all CDN and edge server resources, and present SARENA, an SFC-enabled architecture for adaptive video streaming applications. We equip SARENA’s SDN controller with a lightweight request scheduler and edge configurator to make it deployable in practical environments and to dynamically scale edge servers based on service requirements, respectively. Experimental results show that SARENA outperforms baseline schemes in terms of higher users’ QoE figures by 39.6%, lower E2E latency by 29.3%, and lower backhaul traffic usage by 30% for live and VoD services.
3. Our third contribution aims to use the idle resources of edge servers and employ the capabilities of the SDN controller to establish a collaboration between edge servers in addition to collaboration between edge servers and the SDN controller. We introduce two collaborative edge-assisted frameworks working for HAS-based live or VoD scenarios named LEADER and ARARAT. LEADER utilizes sets of actions (e.g., transcode the requested quality in the local edge server or a neighboring edge server with the highest available resources), presented in a so-called Action Tree, formulates the problem as a central optimization model to enhance the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints, and proposes a lightweight heuristic algorithm to solve the model. ARARAT extends LEADER’s Action Tree, considers network cost in the optimization, devises multiple heuristic algorithms, and runs extensive scenarios. Evaluation results show that LEADER and ARARAT improve users’ QoE by 22%, decrease the streaming cost by 47%, and enhance network utilization by 13%, as compared to their competitors.
4. Our final contribution focuses on incorporating the capabilities of both peer-to-peer (P2P) networks and CDNs, utilizing NFV and edge computing techniques, and then presenting RICHTER and ALIVE as hybrid P2P-CDN frameworks for live streaming scenarios. RICHTER and ALIVE particularly use HAS clients’ (i.e., peers’) potential idle computational resources besides their available bandwidth to provide distributed video processing services, e.g., video transcoding and video super-resolution. Both frameworks introduce multi-layer architectures and design Action Trees that consider all feasible resources (i.e., storage, computation, and bandwidth) provided by peers, edge, and CDN servers for serving clients’ requests with acceptable latency and quality. Moreover, RICHTER proposes an online learning method and ALIVE utilizes a lightweight algorithm distributed over in-network virtualized components, which are designed to play decision-maker roles in large-scale practical scenarios. Evaluation results show that RICHTER and ALIVE improve the users’ QoE by 22%, decrease cost incurred for the streaming service provider by 34%, shorten clients’ serving latency by 39%, enhance edge server energy consumption by 31%, and reduce backhaul bandwidth usage by 24% compared to the baseline approaches.

IEEE Transactions on Multimedia (TMM)

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Yuan Yuan (Southwest Jiaotong University, China), Hongjie He (Southwest Jiaotong University, China), Yaolin Yang (Southwest Jiaotong University, China), Hadi Amirpour (AAU, Klagenfurt, Austria), Christian Timmerer (AAU, Klagenfurt, Austria), Fan Chen (Southwest Jiaotong University, China)

Abstract: Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values. Second, a classification global permutation approach is designed to encrypt the undivided run/size, value (RSV)-based AC groups to achieve larger changes in the block-feature values. Third, to reduce file size expansion while maintaining format compatibility, the DC coefficients are rotated based on the mapped DC differences in the same category, and the nonzero AC coefficients are mapped in the same category. Extensive experiments demonstrate that the proposed algorithm is superior to existing schemes in terms of security. Notably, the average change rate of block-feature values is increased by at least 20%. Furthermore, the proposed scheme reduces the file size by an average of 2.036% compared to existing JPEG image encryption methods.

IEEE Transactions on Network and Service Management 

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Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Shojafar (University of Surrey, UK), Mohammad Ghanbari (University of Essex, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Recent years have witnessed video streaming demands evolve into one of the most popular Internet applications. With the ever-increasing personalized demands for high-definition and low-latency video streaming services, network-assisted video streaming schemes employing modern networking paradigms have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context. The emergence of such techniques addresses long-standing challenges of enhancing users’ Quality of Experience (QoE), end-to-end (E2E) latency, as well as network utilization. However, designing a cost-effective, scalable, and flexible network-assisted video streaming architecture that supports the aforementioned requirements for live streaming services is still an open challenge. This article leverage novel networking paradigms, i.e., edge computing and Network Function Virtualization (NFV), and promising video solutions, i.e., HAS, Video Super-Resolution (SR), and Distributed Video Transcoding (TR), to introduce A Latency- and cost-aware hybrId P2P-CDN framework for liVe video strEaming (ALIVE). We first introduce the ALIVE multi-layer architecture and design an action tree that considers all feasible resources (i.e., storage, computation, and bandwidth) provided by peers, edge, and CDN servers for serving peer requests with acceptable latency and quality. We then formulate the problem as a Mixed Integer Linear Programming (MILP) optimization model executed at the edge of the network. To alleviate the optimization model’s high time complexity, we propose a lightweight heuristic, namely, Greedy-Based Algorithm (GBA). Finally, we (i) design and instantiate a large-scale cloud-based testbed including 350 HAS players, (ii) deploy ALIVE on it, and (iii) conduct a series of experiments to evaluate the performance of ALIVE in various scenarios. Experimental results indicate that ALIVE (i) improves the users’ QoE by at least 22%, (ii) decreases incurred cost of the streaming service provider by at least 34%, (iii) shortens clients’ serving latency by at least 40%, (iv) enhances edge server energy consumption by at least 31%, and (v) reduces backhaul bandwidth usage by at least 24% compared to base line approaches.

Keywords: HTTP Adaptive Streaming (HAS); Edge Computing; Network Function Virtualization (NFV); Content Delivery Network (CDN); Peer-to-Peer (P2P); Quality of Experience (QoE); Video Transcoding; Video Super-Resolution.

Special Issue on Sustainable Multimedia Communications and Services, IEEE COMSOC MMTC Communications – Frontiers,

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Reza Farahani(Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Streaming segmented videos over the Hypertext Transfer Protocol (HTTP) is an increasingly popular approach in both live and video-on-demand (VoD) applications. However, designing a scalable and adaptable framework that reduces servers’ energy consumption and supports low latency and high quality services, particularly for live video streaming scenarios, is still challenging for Over-The-Top (OTT) service providers. To address such challenges, this paper introduces a new hybrid P2P-CDN framework that leverages new networking and computing paradigms, i.e., Network Function Virtualization (NFV) and edge computing for live video streaming. The proposed framework introduces a multi-layer architecture and a tree of possible actions therein (an action tree), taking into account all available resources from peers, edge, and CDN servers to efficiently distribute video fetching and transcoding tasks across a hybrid P2P-CDN network, consequently enhancing the users’ latency and video quality. We also discuss our testbed designed to validate the framework and compare it with baseline methods. The experimental results indicate that the proposed framework improves user Quality of Experience (QoE), reduces client serving latency, and improves edge server energy consumption compared to baseline approaches.

Keywords: Energy Efficiency; HAS; DASH; Edge Computing; NFV; CDN; P2P; Low Latency; QoE; Video Transcoding.

We’re excited to share that the ACM Special Interest Group in Multimedia (SIGMM) presents to

Stefan Lederer, Christopher Müller, and Christian Timmerer

The SIGMM Test of Time Paper Honorable Mention in the category of “MM Systems & Networking”

for their paper “Dynamic Adaptive Streaming over HTTP Dataset”. In Proceedings of the 3rd Multimedia Systems Conference, MMSys ’12, page 89–94, New York, NY, USA, 2012. ACM. doi:10.1145/2155555.2155570