Quality of Experience (QoE) is a key component in adaptive bitrate (ABR) streaming. Whilst the effects of delivery disruptions-such as changes in video quality or rebuffering-have been extensively studied, the impact of playback rate variations remains relatively unexplored. Existing work has examined the QoE impact of playback rate in isolation, without comparing it to other common ABR streaming artifacts such as video quality variations, rebuffering, or seeking. Moreover, the role of gradual playback rate transitions has not been explored. This article addresses these gaps through four large-scale subjective studies that provide a systematic QoE evaluation of playback rate. We compare the acceptability of playback rate changes against quality degradations and rebuffering, and assess rate increases relative to seeking, both of which are strategies for maintaining the desired latency in low-latency streaming. We further investigate how gradual versus instantaneous playback rate transitions affect QoE. Through our subjective studies, we identify levels of slowed-down playback that are imperceptible to users and can be combined with video quality adaptation in ABR algorithms to reduce rebuffering. Moreover, we identify imperceptible rates of speeded-up video playback, which can be used as part of catch-up mechanisms to maintain a desired latency in live streaming, offering a less detrimental alternative to seeking events, which we found to significantly degrade QoE. This work presents the first systematic comparison of playback rate variations with video quality degradation, rebuffering and seeking. The findings extend our understanding of QoE in adaptive video streaming and provide actionable design guidelines for video players to improve the user experience of streaming services.

Traditional media is delivered using segmented video, with variable bitrates, over protocols such as MPEG-DASH. This works well for media experiences with a single, or very few, pre-produced variants. When the number of possible variants increases, however, it results in an explosion of pre-produced whole-experience videos in a one-per-variant relationship; this in turn causes high storage costs and poor re-use of otherwise separable media assets. The paradigm of object-based media (OBM) offers a solution by keeping media entities distinct after production, allowing them to be combined flexibly at the point of consumption. We examine the delivery and playback of OBM using a novel WebAssembly-based media player running in the browser. This approach allows the selective render offload of parts of an experience into the edge/cloud, by migrating associated code from the browser. Using three diverse exemplars of OBM we present a common meta-data format to capture flexible experiences, and measure the performance of our delivery pipeline in a range of on-device and offloaded scenarios. As far as we are aware this is the first such study of generalised OBM media delivery.

In response to the growing threat of cyber-attacks, there is a demand to understand modern, complex attack surfaces. To achieve this, different Internet Intelligence Platforms (IIPs) can be used. In this work, we show there are differences in the attack surfaces created using three IIPs: Shodan, Censys, and ZoomEye. Our results show these discrepancies manifest in the size and temporal characteristics of the identified attack surfaces. This could lead to organisations and decisionmakers potentially being misinformed about their true attack surface. These findings point towards the need for further research focused on understanding these discrepancies.

Driven by the need to better reflect and understand audience quality of experience content providers and deliverers are no longer solely using models for the Mean Opinion Score but also the opinion score distribution. In tandem, motivated by the desire to understand how these models predict the QoE and which features contribute positively to it, there has been increased emphasis on explainable QoE modelling. Recently the advantages of directly explainable models - where model outputs can be explained using the inputs and the model’s inner workings-have been championed. These models provide clear insights into how different features contribute positively, or negatively, to QoE. To date, research into directly explainable methods has focussed on MOS, rather than opinion score distribution, modelling. To bridge this gap we discuss the feasibility of using multinomial logistic regression for directly explainable MOS modelling, demonstrating our approach on a short form streamed video dataset and showing that it compares favourably to other explainable methods.

Despite the rapid growth of 5G technologies, geographical network coverage remains a significant challenge. In certain areas - notably rural - it is anticipated that removing these technologies will result in a complete lack of service. To address this, standards bodies, such as 3GPP, have begun advancing toward 5G-and-beyond architectures incorporating Non-Terrestrial Networks (NTNs), most notably using Low-Earth Orbit (LEO) satellite constellations to expand coverage and improve resilience of 5G terrestrial networks (TN). However, the integration of 5G and NTN introduces new challenges due to the nature of mobility, network characteristics, and deployment costs. To support the development of new 5G-NTN integration architectures, we propose MOSAIC (MObile-SAtellite Integration Cradle), a realistic end-to-end 5G-NTN emulation platform that can recreate the unique features and software of emerging mobile infrastructures. MOSAIC offers a reproducible environment for recreating realistic 5G NTN experiments, utilizing unmodified, off-the-shelf software components. MOSAIC models NTN network characteristics using a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), evaluating it against open-source satellite link measurement data from Starlink. Additionally, using our platform, we assess the performance of the Multipath TCP (MPTCP) protocol to support seamless handover scenarios between TN and NTN. We believe MOSAIC provides a holistic and open environment for experimentation with beyond 5G technologies.

Intent-based networking (IBN) has emerged as a promising paradigm to simplify the management of network infrastructures. At the heart of IBN systems lies the intent handler, a management entity that transforms declarative network goals into imperative network configuration that meets specific policy and optimization requirements. Practical research has produced a number of intent handler implementations, that exhibit specialized use-cases with limited extensibility. This paper presents SWIFT, a framework to simplify the development of custom intent scenarios. To support this capability, SWIFT delivers two main contributions. Firstly, it employs an ontology to abstract several standardized and open network data models (3GPP Core Configuration, ETSI NFV-MANO, ONOS NBI) and support the translation of slice delivery intents, modelled using the TMForum common intent model, into effective infrastructure configurations. Secondly, SWIFT offers a reasoning framework for network operators to implement custom intent handling logic using logical programming. The reasoner can use the rich information of the ontology both to automate the translation of intent requirements, and validate intent goals based on the current network state. Our proof-of-concept SWIFT implementation can automate and optimize the delivery of private 5G network slices. Furthermore, we demonstrate the optimal planning and delivery of mobile slice intents in a real mobile network topology with minimal processing overheads.

User-driven experience customisation can potentially enhance the Quality of Experience (QoE) in personalised multimedia. Such experiences could be, for example, delivered using HTTP Adaptive Streaming - the most prominent way of consuming media over the Internet. In this paper, we present a subjective study designed to investigate the QoE impact of user-driven customisation in personalised media experiences. We offered participants the option to customise their video layout, after which we asked them to score a range of quality impairments found in HTTP Adaptive Streaming. Based on our analysis of the collected Mean Opinion Scores (MOS), we found that experience customisation impacts the QoE in a surprising way. We found that experience personalisation caused participants’ expectations to increase when it comes to QoE, as they perceived the most severe quality impairments worse than the control. This establishes the need for specialised QoE models that take into account different levels of user expectations.

Quality of Experience (QoE) is a crucial component of adaptive bitrate (ABR) streaming, with the effects of abrupt changes in playback quality or rebuffering, caused by delivery disruptions, being widely studied. However, the collective ABR community has a limited understanding of the effects of changes in playback rate on QoE. In this pioneering work, we investigate two aspects of playback rate fluctuations. In particular, we carry out two subjective studies to assess if a change in playback rate is more or less acceptable than a drop in video quality or a rebuffering event. Furthermore, we examine the effect of the transition in playback rate on QoE, comparing gradual and instant variations. Our subjective studies recruited 120 participants who evaluated 102 test sequences. In summary, we find that playback rate drops of 0.8-0.9 are imperceptible for most content, and rated similarly to a video quality drop to medium level. In contrast, lower playback rates of 0.6-0.7 were perceived as poorly as rebuffering events. Gradual changes in playback rate can offer better QoE, but only in limited cases depending on the content, target playback rate, as well as magnitude of change.

Multi-agent, decentralised, and collective self-adaptive systems have been studied in a range of domains from smart-cities to the social dynamics of organisations. We present a novel application for research in this area, with the emerging distributed systems field of object-based media. Unlike traditional media, which is delivered over the Internet as pre-encoded compressed video segments, object-based media divides a media experience into its constituent parts, such as presenters, actors, backgrounds, and information overlays. These elements are then rendered and composited on-demand for each user, to support highly customisable experiences. We analyse the distributed systems challenges of delivering object-based media in terms of self-adaptive systems, and present work on the prototype technology we are using to explore potential solutions.

Intent-based networking (IBN) has emerged as a promising paradigm to improve management automation in network infrastructures. At the heart of IBN systems lies the intent handler, an intelligent translation process that transforms declarative network goals into imperative network configurations. Although IBN research has demonstrated the ability to support complex automation scenarios, existing intent handlers have limited scope, targeting specialized use cases and optimizations, and offering limited extensibility. This paper presents Oz, an intent framework baed on Semantic Web technologies. Oz uses an ontology to combine several standardized data models and support the translation of TMForum common intent model expressions into effective infrastructure configurations. Furthermore, Oz implements a semantic web reasoner that allows operators to realize intents using logic rules that interpret intent requirements into configuration and validate intent fulfillment based on the current network state. Using a production network topology, we demonstrate that Oz can plan the deployment of a cache service intent on a busy network in less than 10 seconds, by using a contextual state exploration intent implementation.

The surge in popularity of live video streaming has spurred the development of various bitrate adaptation techniques, all aimed at enhancing user Quality of Experience (QoE). Compared to streaming Video-on-Demand, achieving low-latency live video streaming under fluctuating network conditions poses additional challenges. It requires finding the balance between rebuffering avoidance and latency, as a small client buffer is required to achieve low latency. Video players can also employ playback speed control to help optimize this balance. Specifically, when client buffer occupancy is high and hence latency is high, the player may increase playback speed to reduce the latency; and conversely, when client buffer occupancy is low and hence the risk of rebuffering is high, the player may reduce playback speed to increase buffer occupancy. Based on this rationale, a variety of playback speed control methods have been proposed. This paper evaluates, using a real-world testbed, the effectiveness of various playback speed control mechanisms when applied to a set of bitrate adaptation algorithms, with the evaluation also encompassing variations in target latency and network conditions. Our findings show a lack of coordination between adaptive bitrate (ABR) algorithms and playback speed control mechanisms. This leads us to conclude that there is a need for new playback speed control methods designed in conjunction with ABR algorithms.

Industry 4.0 and the trend of connecting legacy Industrial Control Systems (ICSs) to public networks have exposed these systems to various online threats. To combat these threats, honeypots have been widely used to provide proactive monitoring, detection and deception security capabilities. However, skilled attackers are now adept at fingerprinting and avoiding honeypots. Therefore, we take a fundamentally different approach in this paper. Instead of the honeypot representing a real system, we deploy it as a deterrent. Through obfuscation, the aim is to make an attacker believe the real system is a honeypot and collect threat intelligence data on the attacker. To achieve this, we introduce a new obfuscation technique that allows real ICSs to present themselves as honeypots. By taking advantage of honeypot fingerprinting techniques, we are able to deter attackers from interacting with the real Programmable Logic Controller (PLC) within the industrial network. The approach is implemented and evaluated using different penetration testing tools and an expert evaluation highlighting the benefits of obfuscation in that potential adversaries would be misled into assuming the PLC is a honeypot.

Cloud computing has grown in importance in recent years which has led to a significant increase in Data Centre (DC) network requirements. A major driver of this change is virtualisation, which allows computing resources to be deployed on a large scale. However, traditional DCs, with their network topology and proliferation of network endpoints, are struggling to meet the flexible, centrally managed requirements of cloud computing applications. Software-Defined Networks (SDN) promise to offer a solution to these growing networking requirements by separating control functions from data routing. This shift adds more flexibility to networks but also introduces new security issues. This article presents a framework for evaluating security of SDN architectures. In addition, through an experimental study, we demonstrate how this framework can identify the threats and vulnerabilities, calculate their risks and severity, and provide the necessary measures to mitigate them. The proposed framework helps administrators to evaluate SDN security, address identified threats and meet network security requirements.

The ever-growing volume of network traffic and widening adoption of Internet protocols to underpin common communication processes augments the importance of network security. In order to enforce network security policies, network managers adopt a widening set of middleboxes and network appliances to improve traffic monitoring and processing capabilities. The resource requirements to support network security appliances are constantly increasing, making efficiency of these systems an essential aspect. The move toward Software-Defined Networking and programmable data planes offers a mean to offload traffic processing functionalities to within the network itself. To this end, we present the 4MIDable framework: a platform that facilitates the integration of existing middleboxes and monitoring appliances with an SDN (P4) network infrastructure. We also present P4Protect, a 4MIDable agent that protects the network from control plane DoS attacks with negligible impact on control plane latency, and P4ID (P4-Enhanced Intrusion Detection), a 4MIDable agent that offers stateful processing and feedback to unmodified Intrusion Detection System middleboxes and reduces traffic processing by over 80% without affecting threat detection rates.

The importance of cloud computing has grown over the last years, which resulted in a significant increase of Data Center (DC) network requirements. Virtualisation is one of the key drivers of that transformation and enables a massive deployment of computing resources, which exhausts server capacity limits. Furthermore, the increased network endpoints need to be handled dynamically and centrally to facilitate cloud computing functionalities. Traditional DCs barely satisfy those demands because of their inherent limitations based on the network topology. Software-Defined Networks (SDN) promise to meet the increasing network requirements for cloud applications by decoupling control functionalities from data forwarding. Although SDN solutions add more flexibility to DC networks, they also pose new vulnerabilities with a high impact due to the centralised architecture. In this paper we propose an evaluation framework for assessing the security level of SDN architectures in four different stages. Furthermore, we show in an experimental study, how the framework can be used for mapping SDN threats with associated vulnerabilities and necessary mitigations in conjunction with risk and impact classification. The proposed framework helps administrators to evaluate the network security level, to apply countermeasures for identified SDN threats, and to meet the networks security requirements.

Network softwarization has revitalized the interest of the network community towards emulation as an effective mechanism for network experimentation. Relevant platforms automate the deployment of virtual network topologies on a host, providing users the ability to manually run experimental scenarios. Whilst this may suit prototyping, modern development and deployment practices such as CI/CD depend on fully automated testing processes, built around high-level testing APIs and abstracting the challenges involved with synchronizing complex node interaction scenarios. In this paper, we present Network Emulation System (NES): a cloud-native, and highly-parallelizable Network Emulation as a Service (NEaaS) platform designed from the ground up to facilitate codeless experiment specification and to automate network testing workflows in cloud CI/CD environments. We demonstrate that NES offers a 8x speedup improvement in topology instantiation times in comparison to existing emulation platforms, and its life-cycle model can automate testing processes for complex service configurations using existing CI/CD platforms such as GitHub Actions.

Video streaming continues to be the largest service delivered on the internet. This includes gaming videos, delivered both on-demand and live, where gaming footage is usually accompanied by a video of the player overlaid on top of the gameplay - resulting in Picture-In-Picture (PiP) content. Currently, PiP content is usually combined into a single video before being delivered to the client via technologies such as HTTP Adaptive Streaming (HAS). In this study, we investigated the QoE importance of gameplay and player elements in PiP gaming videos by varying the video quality of these elements individually. We conducted a subjective study, testing nine quality permutations based on three quality levels across three pieces of content from different gaming genres, with 30 participants recruited using an ethical crowdsourcing platform. We found that gameplay was significantly more important in terms of overall QoE, while the player element made a difference in only a few cases.

HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (ABR) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the ABR algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the ABR needs to respond quickly. Llama (Lyko et al. 28) is a new low latency ABR algorithm that we have previously proposed and assessed against four on-demand ABR algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency ABR algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from ABR behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.

Ever since the first automation provided by the introduction of the Strowger telephone exchange in the late 19th century, networks have been increasingly automated. Fast forward to 2022, and the challenge facing network providers is scaling up this level of automation considering massive increases in complexity, new levels of agility to operate services, and rising demand from customers within the modern telecommunications ecosystem. This article describes a significant new industry-academia partnership to address these challenges: Next Generation Converged Digital Infrastructure (NG-CDI) is creating a vision for the building and operation of a future-proof network infrastructure and its autonomic management. In this article, we highlight three exemplar activities within the NG-CDI research program that illustrate the benefits of taking a highly collaborative interdisciplinary approach and show how academia and industry working closely together have delivered a range of direct and positive impacts on business.

Intent-based networking (IBN) systems have become the de-facto control abstraction to drive self-service, self-healing, and self-optimized capabilities in service delivery processes. Nonetheless, the operation complexity of modern network infrastructures make network practitioners apprehensive towards adoption in production, requiring further evidence for correctness. In this paper, we argue that testing, verification and monitoring should become first-class citizens in reference IBN architecture, in order to improve the detection errors during operations. Towards this goal, we present an extension for an intent architecture that allows IBN system to validate the correctness of network configuration using realistic network emulation. Furthermore, we present an intent use-case that ensure correct operation in hybrid networks.