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

The 19th International Conference on emerging Networking EXperiments and Technologies

Leonardo Peroni (IMDEA Networks Institute and UC3M), Sergey Gorinsky (IMDEA Networks Institute), Farzad Tashtarian (AAU, Austria), and Christian Timmerer (AAU, Austria).

Abstract: Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.

Klagenfurt, June 7, 2023

Congratulations to Dr. Ekrem Çetinkaya for successfully defending his dissertation on “Video Coding Enhancements for HTTP Adaptive Streaming using Machine Learning” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA.

Abstract

Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video.
As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance.

This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning. These contributions are presented in four categories:

1. Fast Multi-Rate Encoding with Machine Learning: This category consists of two contributions that target the need for encoding multiple representations of the same video for HTTP adaptive streaming. FaME-ML tackles the multi-rate encoding problem using convolutional neural networks to guide encoding decisions, while FaRes-ML extends the solution for multi-resolution scenarios. Evaluations showed FaME-ML could reduce parallel encoding time by 41% and FaRes-ML could reduce overall encoding time by 46% while preserving the visual quality.
2. Enhancing Visual Quality on Mobile Devices: The second category consists of three contributions targeting the need for the improved visual quality of videos on mobile devices. The limited hardware of mobile devices makes them a challenging environment to execute complex machine learning models. SR-ABR explores the integration of the super-resolution approach into the adaptive bitrate selection algorithm. SR-ABR can save up to 43% bandwidth. LiDeR is addressing the computational complexity of super-resolution networks by proposing an alternative that considers the limitations of mobile devices by design. LiDeR can increase execution speed up to 428% compared to state-of-the-art networks while managing to preserve the visual quality. MoViDNN is proposed to enable straightforward evaluation of machine learning-based solutions for improving visual quality on mobile devices.
3. Light-Field Image Coding with Super-Resolution: Emerging media formats provide a more immersive experience with the cost of increased data size. The third category proposes a single contribution to tackle the huge data size of light field images by utilizing super-resolution. LFC-SASR can reduce data size by 54% while preserving the visual quality.
4. Blind Visual Quality Assessment Using Vision Transformers: The final category consists of a single contribution that is proposed to tackle the blind visual quality assessment problem for videos. BQ-ViT utilizes recently proposed vision transformer architecture. It can predict the visual quality of a video with a high correlation (0.895 PCC) by using only the encoded frames.

The thesis is available for download here. Slides and video are available as follows:

Klagenfurt, July 10, 2023

Congratulations to Dr. Jesús Aguilar Armijo for successfully defending his dissertation on “Multi-access Edge Computing for Adaptive Video Streaming” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA.

Abstract

Over the last recent years, video streaming traffic has become the dominating service over mobile networks. The two main reasons for the growth of video streaming traffic are the improved capabilities of mobile devices and the emergence of HTTP Adaptive Streaming (HAS). Hence, there is a demand for new technologies to cope with the increasing traffic load while improving clients’ Quality of Experience (QoE). The network plays a crucial role in the video streaming process. One of the key technologies on the network side is Multi-access Edge Computing (MEC), which has several key characteristics: computing power, storage, proximity to the clients and access to network and player metrics. Thus, it is possible to deploy mechanisms at the MEC node that assist video streaming.

This thesis investigates how MEC capabilities can be leveraged to support video streaming delivery, specifically to improve the QoE, reduce latency or increase storage and bandwidth savings. This dissertation proposes four contributions:

1. Adaptive video streaming and edge computing simulator: A simulator named ANGELA, HTTP Adaptive Streaming and Edge Computing Simulator, was designed to test mechanisms running at the edge node that support video streaming. ANGELA overcomes some issues with state-of-the-art simulators by offering: (i) access to radio and player metrics at the MEC node, (ii) different configurations of multimedia content (e.g., bitrate ladder or video popularity distribution), (iii) support for Adaptive Bitrate (ABR) algorithms at different locations of the network (e.g., server- based, client-based and network-based) and (iv) a wide variety of evaluation metrics. ANGELA uses real 4G/5G network traces to simulate the radio layer, which offers realistic results without simulating the complex processes of the radio layer. Testing a simple MEC mechanism scenario showed a simulation time decrease of 99.76% in ANGELA compared to the simulation using the state-of-the-art simulator ns-3.
2. Dynamic segment repackaging at the edge: Adaptive video streaming supports different media delivery formats such as HTTP Live Streaming (HLS) [11], Dynamic Adaptive Streaming over HTTP (MPEG-DASH), Microsoft Smooth Streaming (MSS) and HTTP Dynamic Streaming (HDS). This contribution proposes using the Common Media Application Format (CMAF) in the network’s backhaul, performing a repackaging to the clients’ requested delivery format at the MEC node. The main advantages of this approach are bandwidth savings at the network’s backhaul and reduced storage costs at the server and edge side. According to our measurements, the proposed model will also reduce delivery latency if the edge has more than 1.64 times the compute power per segment than the origin server, which is expected due to lower load.
3. Edge-assisted adaptation schemes: The radio network and player metrics infor- mation available at the MEC node is leveraged to perform better adaptation decisions. Two edge-assisted adaptation schemes are proposed: EADAS, which improves ABR decisions on the fly to increase clients’ QoE and fairness, and ECAS-ML, which moves the whole ABR algorithm logic to the edge and manages the tradeoff among bitrate, segment switches and stalls to enhance QoE. To accomplish that, ECAS-ML utilizes machine learning techniques to analyze the radio network throughput and predict the algorithm parameters that provide the highest QoE. Our evaluation shows that EADAS enhances the performance of ABR algorithms, increasing the QoE by 4.6%, 23.5%, and 24.4% and the fairness by 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively. Moreover, ECAS-ML shows a QoE increase of 13.8%, 20.85%, 20.07% and 19.29% against a buffer-based, throughput-based, hybrid-based and edge-based ABR algorithm, respectively.
4. Segment prefetching and caching at the edge: Segment prefetching is a technique that consists of transmitting future video segments to a location closer to the client before they are requested. Hence, the segments are served with reduced latency. The MEC node is an ideal location for performing segment prefetching and caching due to its proximity to the client, its access to radio and player metrics and its storage and computing capabilities. Several segment prefetching policies that use different types and amounts of resources and are based on different techniques, such as a Markov prediction model, machine learning, transrating (i.e., reducing segment bitrate/quality) or super-resolution, are proposed and evaluated. Moreover, the influence on segment prefetching of the caching policy, the bitrate ladder and the chosen ABR algorithm is studied. Results show that the segment prefetching based on machine learning increases the average bitrate by ≈46% while reducing the average number of stalls by ≈20% only increasing the extra bandwidth consumption by ≈6% regarding the baseline simulation with no segment prefetching. Other prefetching policies offer a different combination of performance enhancement and resource usage that can adapt to the service provider’s needs.

Each of these contributions focuses on a different aspect of content delivery for video streaming but can be used jointly to improve video streaming services using MEC capabilities.

EADAS and ECAS-ML can improve the quality adaptation decisions and enable segment prefetching compatibility without the throughput miscalculation issues of client- based ABR algorithms. Moreover, the dynamic repackaging mechanism can be used jointly with segment prefetching and edge-based adaptation schemes to increase bandwidth savings in the backhaul, which reduces the negative impact of some segment prefetching policies.