We provide an overview of an architecture of today's Internet streaming media delivery networks and describe various problems that such systems pose with regard to video coding. We demonstrate that based on the distribution model (live or on-demand), the type of the network delivery mechanism (unicast versus multicast), and optimization criteria associated with particular segments of the network (e.g., minimization of distortion for a given connection rate, minimization of traffic in the dedicated delivery network, etc.), it is possible to identify several models of communication that may require different treatment from both source and channel coding perspectives. We explain how some of these problems can be addressed using a conventional framework of temporal motion-compensated, transform-based video compression algorithm, supported by appropriate channel-adaptation mechanisms in client and server components of a streaming media system. Most of these techniques have already been implemented in RealNetworks(R) RealSystem(R) 8 and its RealVideo(R) 8 codec, which we use throughout the paper to illustrate our results.

Video Coding for Streaming Media Delivery on the Internet

A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets

In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks.To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.

Beyond throughput: a 4G LTE dataset with channel and context metrics

The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers’ needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high-level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

HTTP adaptive streaming (HAS) is the most recent attempt regarding video quality adaptation. It enables cheap and easy to implement streaming technology without the need for a dedicated infrastructure. By using a combination of TCP and HTTP it has the advantage of reusing all the existing technologies designed for ordinary web. Equally important is that HAS traffic passes through firewalls and works well when NAT is deployed. The rate adaptation controller of HAS, commonly called adaptive bitrate selection (ABR), is currently receiving a lot of attention from both industry and academia. However, most of the research efforts concentrate on a specific aspect or a particular methodology without considering the overall context. This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming. It starts by decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module. Each subcomponent encapsulates a particular function that is vital to the operation of an ABR scheme. A review of each of the subcomponents and how they interact with each other is presented. Furthermore, those external factors that are known to have a direct impact on the performance of an ABR module, such as content nature, CDN, and context, are discussed. In conclusion, this paper provides an extensive reference for further research in the field.

Adaptive Bitrate Selection: A Survey

The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bit-rate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their users' streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that SMASH guarantees a high QoE with consistent performance across a variety of streaming contexts.

SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP

A provision-aware fair bandwidth distribution marker algorithm for DiffServ networks

Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users' demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P. 1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms.

DASHbed: a testbed framework for large scale empirical evaluation of real-time DASH in wireless scenarios

From the thermodynamic and kinetic viewpoints, the oxygen evolution reaction (OER) is central to the production of hydrogen through electrocatalytic water splitting process. As a result, extensive research is carried out with an aim to develop (preferably low cost) electrocatalysts for efficient OER reaction. Two main mechanisms, the lattice oxygen mediated mechanism (LOM) and the adsorbate evolution mechanism (AEM), are proposed for the OER process, but the former seems to be favored as the latter is surrounded by a number of discrepancies. The LOM is based on the oxidation and reduction chemistry of lattice oxygen anions. Both from fundamental and application perspectives, identification and selection of crucial electronic and structural features to rationally tune the LOM for OER process and determine the rate of the OER reaction are essential but remain a challenge. As yet, this has largely been attempted by trial and error (synthesis and performance evaluation) approaches and, hence, has been tied up by tediousness and inefficiency. Considering the availability of large chemical space and huge probabilities in fine-tuning of electronic and structural attributes, the use of artificial intelligence (AI) can be efficacious. To predict promising material features that enable rational design of OER electrocatalysts, electrocatalytic performance maps have been developed based on reactivity multiple descriptors. The reactivity multiple descriptors are numerous and are classified into several families. Therefore, modern materials informatics technique is urgently required to enable large-scale data mining to rapidly screen and select the best physical features from multiple descriptors for a rational design of LOM-induced OER electrocatalysts. In this context, feature selection by artificial intelligence-based approach can be a solution to such challenge. Yet to the best of our knowledge, no attempt has been made before to tune electrocatalysts for the OER through LOM reaction mechanism following feature selection by artificial intelligence-based approach. 

Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach

HTTP adaptive video streaming matches video quality to the capacity of a changing context. A variety of schemes that rely on buffer state dynamics for video rate selection have been proposed. However, these schemes are predominantly based on heuristics, and appropriate models describing the relationship between video rate and buffer levels have not received sufficient attention. In this paper, we present a QoE-aware video rate evolution model based on buffer state changes. The scheme is evaluated within a real-world Internet environment. The results of an extensive evaluation show an improvement in the stability, average video rate and system utilisation, while at the same time a reduction in the start-up delay and convergence time is achieved by the modified players.

/pdf/on-the-trajectory-of-video-quality-transition-in-http-45vfr84ioa.pdf

Yusuf Sani

Papers

SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP

A provision-aware fair bandwidth distribution marker algorithm for DiffServ networks

DASHbed: a testbed framework for large scale empirical evaluation of real-time DASH in wireless scenarios

Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach

On the trajectory of video quality transition in HTTP adaptive video streaming