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.

Klagenfurt, May 25, 2023

Congratulations to Dr. Alireza Erfanian for successfully defending his dissertation on “Optimizing QoE and Latency of Video Streaming using Edge Computing and In-Network Intelligence” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA.

Abstract:

Nowadays, HTTP Adaptive Streaming (HAS) has become the de-facto standard for delivering video over the Internet. More users have started generating and delivering high-quality live streams (usually 4K resolution) through popular online streaming platforms, resulting in a rise in live streaming traffic. Typically, the video contents are generated by streamers and watched by many audiences, geographically distributed in various locations far away from the streamers. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. This dissertation leverages edge computing capabilities and in-network intelligence to design, implement, and evaluate approaches to optimize Quality of Experience (QoE) and end-to-end (E2E) latency of live HAS. In addition, improving transcoding performance and optimizing the cost of running live HAS services and the network’s backhaul utilization are considered. Motivated by the mentioned issue, the dissertation proposes five contributions in two classes: optimizing resource utilization and light-weight transcoding.

Optimizing resource utilization: This class consists of two contributions, ORAVA and OSCAR. They leverage in-network intelligence paradigms, i.e., edge computing, Network Function Virtualization (NFV), and Software Defined Networking (SDN) to introduce two types of Virtual Network Functions (VNFs): Virtual Reverse Proxy
(VRP) and Virtual Transcoder Functions (VTFs). At the network’s edge, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. The SDN controller then creates a multicast tree from the origin server to the optimal set of VTFs, delivering only the highest requested bitrate to elevate the efficiency of resource allocation. The selected VTFs transcode the received segment to the requested bitrate and transmit it to the corresponding VRPs. The problem of determining multicast tree(s) and selecting VTFs has been formulated as a Mixed- Integer Linear Programming (MILP) optimization problem, aiming to minimize the streaming cost and resource utilization while considering delay constraints.

1. ORAVA: It presents a cost-aware approach to provide Advanced Video Coding (AVC)-based real-time video streaming services in the network. It transmits
   the generated bitrates from VTFs to corresponding VRPs in a unicast manner.
2. OSCAR: It extends ORAVA by introducing a new SDN-based live video streaming approach. Instead of unicast transmission, it streams requested bitrates from VTFs to VRPs in a multicast manner, resulting in lower bandwidth consumption. It is also able to use VTFs with different types of virtual machine instances (i.e., CPU or memory resources) to reduce the total service cost.

According to evaluation results, ORAVA and OSCAR save up to 65% bandwidth compared to state-of-the-art approaches; furthermore, they reduce the number of generated OpenFlow (OF) commands by up to 78% and 82%, respectively.

Light-weight transcoding: This class consists of three contributions, named LwTE, CD-LwTE, and LwTE-Live. Employing edge computing and NFV, they introduce a
novel transcoding approach that significantly saves transcoding time and cost.

1. LwTE: It introduces a novel Light-weight Transcoding approach at the Edge in the context of HAS. During the encoding process of a video segment at the origin side, computationally intense search processes are going on. It stores the optimal results of these search processes as metadata for each video bitrate and reuses them at the edge server to reduce the required time and computational resources for transcoding. It applies a store policy on popular segments/bitrates to cache them at the edge, and a transcode policy on unpopular ones that stores the highest bitrate plus corresponding metadata (of very small size).
2. CD-LwTE: This contribution extends the investigation on LwTE by proposing Cost- and Delay-aware Light-weight Transcoding at the Edge. As an extension, it introduces resource constraints at the edge and considers a new policy (i.e.,
   fetch policy) for serving requests at the edge. In the same direction, it also adds serving delay to the objective of selecting an appropriate policy for each segment/bitrate, aiming to minimize the total cost and serving delay.
3. LwTE-Live: It investigates the cost efficiency of LwTE in the context of live HAS. It utilizes the LwTE approach to save bandwidth in the backhaul network, which may become a bottleneck in live video streaming.

The evaluation results show that LwTE does the transcoding processes at least 80% faster than the conventional transcoding method. By adding new features in the metadata, CD-LwTE reduces the transcoding time by up to 97%. Moreover, it decreases the streaming costs, including storage, computation, and bandwidth costs, by up to 75%, and reduces delay by up to 48% compared to state-of-the- art approaches.

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

Klagenfurt, June 30, 2023

Congratulations to Dr. Minh Nguyen for successfully defending his dissertation on “Policy-driven Dynamic HTTP Adaptive Streaming Player Environment” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA.

Abstract

In the last decades, video streaming has been developing significantly. Among cur- rent technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side. The main contributions of this thesis are four-fold:

1. This thesis demonstrates that HTTP/3’s features can be utilized to consider- ably enhance the QoE of the end user by improving the video quality. In a streaming session, the end user would have to download low-quality segments, which impair the QoE, due to the throughput fluctuation. We propose Days of Future Past Plus (DoFP+) approach that leverages HTTP/3’s features to upgrade low-quality segments while downloading others. The experimental results reveal a boost in QoE by as much as 33%. In addition, DoFP+ saves an average of 16% of the downloaded data for all test videos. The findings indicate that the sequential download of segments is more advantageous for re- transmissions compared to concurrent downloads, and upgrading lower-quality segments first will result in a more remarkable improvement in QoE.
2. This thesis proposes a weighted sum model, namely WISH, to provide a high QoE of the video and allow end users to express their preferences among different parameters, including data usage, stall events, and video quality. WISH takes into account three distinct cost elements, namely throughput cost, buffer cost, and quality cost, for each quality version and integrates them into a weighted sum as the overall cost. The results of the experiments indicate that WISH enhances the QoE by up to 17.6% while at the same time reducing data usage by 36.4% in comparison to state-of-the-art approaches. It also offers a dynamic adaptation to meet the demands of end users.
3. To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side. WISH-SR extends WISH by utilizing CNN-based Super Resolution (SR) models deployed in mobile devices to improve video quality while remarkably reducing the volume of data transmitted. WISH-SR has the capacity to reduce up to 43% of the total downloaded data and enhance the visual quality compared to WISH.
4. Finally, this thesis presents an approach to determine Common Media Client Data (CMCD) parameters from the client and process them at the server for the purpose of generating a suitable bitrate ladder for each client. The bitrate ladder is created based on clients’ device types and network conditions. Our approach is able to reduce the downloaded data while improving the QoE as much as 2.6 times.

IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, South Korea

[PDF]

Lingfeng Qu (SWJTU, China), Hongjie He (SWJTU, China), Hadi Amirpour (AAU, Austria), Mohammad Ghanbari (University of Essex, UK) , and Christian Timmerer (AAU, Austria)

Abstract: In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π2 and the bit-plane permutation sequence Π1. Our experiments show that the accuracy of estimating Π2 is higher than 70% for block sizes of 3×3 pixels or larger. Despite a 40% accuracy in estimating Π1, the content information of the ciphertext image can still be exposed.

IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, Korea

[PDF]

Jingwen zhu (Nantes University), Hadi Amirpour (AAU, Austria), Raimund Schatz (AIT, Austria) ,  Christian Timmerer (AAU, Austria), and Patrick Le Callet (Nantes University)

Abstract: In adaptive video streaming, optimizing the selection of representations for the encoding bitrate ladder has a significant impact on the quality and economics of media delivery. An efficient way to select representations for the bitrate ladder of a given clip is to consider the Satisfied User Ratio (SUR) of the perceived quality of consecutive representations. This ensures that only representations with one Just Noticeable Difference (JND) are encoded and streamed by avoiding encoding similar-quality representation. VMAF (Video Multi-method Assessment Fusion) presently stands as the most commonly utilized quality metric for constructing bitrate ladders. Hence, the precise determination of JND-optimal encoding step-sizes for the VMAF proxy holds paramount importance; nevertheless, this task is intricate and can present considerable challenges. In this paper, we evaluate the effectiveness of different Video Quality Metrics (VQM) in predicting SUR for the VMAF proxy to better capture content-specific characteristics. Our experimental results provide evidence that incorporating VQM can improve the precision of the SUR prediction for the VMAF proxy. Compared to a state-of-the-art approach that utilizes video complexity metrics, our proposed approach, which incorporates two quality metrics—specifically, VMAF and SSIM calculated at an optimized quantization parameter (QP)—achieves a substantially reduced Mean Absolute Error (MAE) of 1.67. In contrast, the state-of-the-art approach yields an MAE of 2.01. Hence, we recommend using the above quality metrics to improve the accuracy of SUR prediction for the VMAF proxy.

IEEE International Conference on

Visual Communications and Image Processing (VCIP)

03-07 December 2023, Jeju, Korea

[PDF]

Yiqun Liu (Ateme), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohsen Abdoli (IRT b-com),  Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Thomas Guionnet (Ateme)

Abstract: The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like VVC poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in VMAF. This improvement is observed when utilizing VVenC, an open and optimized VVC encoder implementation.