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.

/pdf/qoe-management-of-multimedia-streaming-services-in-future-17vxrwazos.pdf

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

Video content is increasingly being consumed on the move using mobile devices such as smartphones and tablets. In order to deal with the challenges of heterogeneity of network access technologies and fluctuating resources, which are inherent features of mobile communication, HTTP adaptive streaming (HAS) is becoming the default technology for online video streaming. However, little research has been carried out to better understand the impact of handover schemes of the various mobility management protocols on the video quality of HAS. In this study, the authors present a comprehensive experimental measurement of the impact of handover on three representative HAS players. First, they implement three existing mobility management protocols, MIPv6, LISP-MN and PMIPv6, on a network testbed. Using the fluid flow mobility model, the impact of frequent handover on the average video quality, the bandwidth utilisation and stability of the players was investigated. Their results show a degradation of all the observed parameters in all the reviewed players.

/pdf/experimental-evaluation-of-the-impact-of-mobility-management-18hocpfs9t.pdf

Experimental evaluation of the impact of mobility management protocols on HTTP adaptive streaming

Nowadays, a typical video content provider serves a variety of platforms e.g. smartphones, web browsers, and smart TVs. Each of these platforms has specific requirements with respect to transmission and video quality. Moreover, since these devices are increasingly being used on-the-go, the environment within which most of these video streaming clients operate is both unreliable and time-varying. To cater for these heterogeneous requirements, content providers are increasingly adopting adaptive streaming services. Through such services, the quality of the video content received by a user is adapted to fit its specific requirements and capabilities. To adapt the video quality, system capabilities such as network capacity and memory have to be continuously monitored and measured, chunk requests have to be scheduled, and then the optimal video rate has to be decided. Each of these tasks is usually managed by a sub-module of the adaptive bitrate selection function. However, these sub-components interact in a non-trivial manner. For example, while on-off chunk scheduling helps to prevent buffer overflow, it negatively affects the TCP throughput. Hence, these complex interactions between these different sub-components of the adaptive streaming algorithm result in unnecessary rebufferings, undesirable variability, and sub-optimal video quality. To help simplify these interactions, this thesis develops several frameworks and models that define the relationships between the various components of the adaptive bitrate selection system. This includes deriving the valid system state space, which defines the state that an algorithm can be in at any given time, determining the allowable interactions between the various components, and identifying the video quality evolution rules that optimise QoE. Using this information, some state-of-the-art algorithms are improved and novel ones developed to demonstrate the effectiveness of the proposed approach. The result of extensive evaluations conducted both within a real-world Internet environment and with network trace shows the proposed schemes help in reducing the convergence time, startup delay, and rebuffering events, while at the same time increasing both the average and the stability of the video quality. All this is obtained without any adverse impact on the fairness among the competing players.

Advanced modelling of adaptive bitrate selection

Host mobility protocols such as Locator-Identifier Separation Protocol Mobile Node (LISP-MN) are known to experience packet loss at the point of handover. For the duration of the handover, packets sent to the MN via the old access link are dropped by the router since it has no way of knowing where the device has moved to. This affects the performance of transport layer protocols of the TCP/IP stack, which results in degradation of network performance. Buffering these packets close to the MN's new location and forwarding them to the MN on handover completion is one way of improving the overall performance of the mobility protocol. Hence, we introduce a novel network node, loc-server, to buffer these packets in order to mitigate the packet loss and reduce the service disruption time (SDT) experienced by MNs during handovers. Using a laboratory testbed implementation, LISP-MN with loc-server support shows significant reduction in packet loss and reduced SDT in comparison to vanilla LISP-MN. Similarly, performance analysis of DASH video player also shows the new architecture helps in improving the average video quality downloaded by the MN and reduces the player's instability.

/pdf/towards-zero-packet-loss-with-lisp-mobile-node-4qydudqtl9.pdf

Towards zero packet loss with LISP Mobile Node

With increased awareness of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) can facilitate fast development of high-performance electrochemical energy storage systems (EESSs).

/pdf/artificial-intelligence-navigated-development-of-high-1m3ole56.pdf

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

Yusuf Sani

Papers

Experimental evaluation of the impact of mobility management protocols on HTTP adaptive streaming

Advanced modelling of adaptive bitrate selection

Towards zero packet loss with LISP Mobile Node

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

A bio-inspired managed video delivery service using HTTP-based adaptive streaming