Innovative algorithms for prioritised AR/VR content delivery

This paper proposes, describes and analyses two innovative approaches which make use of the Multipath Transmission Control Protocol (MPTCP) and its multiple flows for prioritised AR/VR content delivery. The first approach involves delivery of prioritised data using a fixed subflow and therefore establishment of a virtual private channel (VPC) for sending data. The second approach introduces a novel QoS on-the-fly (QoSF) algorithm that evaluates all subflows’ delivery performance and dynamically selects one of them to deliver the prioritised content with the lowest latency. Both algorithms are assessed in singlehomed and multi-homed configurations and present different performance improvements in comparison to the classic MPTCP. While QoSF algorithm demonstrates the best performance in scenarios where the delivery latency variation between subflows is high, the VPC algorithm has the best results in scenarios where this variation is smaller

Innovative algorithms for prioritised AR/VR content delivery

This paper proposes, describes and analyses two innovative approaches which make use of the Multipath Transmission Control Protocol (MPTCP) and its multiple flows for prioritised AR/VR content delivery. The first approach involves delivery of prioritised data using a fixed subflow and therefore establishment of a virtual private channel (VPC) for sending data. The second approach introduces a novel QoS on-the-fly (QoSF) algorithm that evaluates all subflows' delivery performance and dynamically selects one of them to deliver the prioritised content with the lowest latency. Both algorithms are assessed in single-homed and multi-homed configurations and present different performance improvements in comparison to the classic MPTCP. While QoSF algorithm demonstrates the best performance in scenarios where the delivery latency variation between sub flows is high, the VPC algorithm has the best results in scenarios where this variation is smaller

An Innovative algorithm for improved quality multipath delivery of virtual reality content

This paper describes and evaluates an Innovative Algorithm for Improved Quality Multipath Delivery of Virtual Reality Content (QM4VR) that addresses the stringent communication requirements of Virtual Reality (VR) applications. Making use of the Multipath TCP (MPTCP) built-in multipath delivery features (subflows), QM4VR explores the subflows’ characteristics, evaluates their performance (e.g., delay, throughput or loss) and proposes a new management scheme to improve the Quality of Service (QOS) of the VR applications. glsqm4vr adopts a Machine Learning (ML)-based approach to evaluate the subflows’ performance which is implemented in two steps: 1) a linear regression scheme to forecast the subflow’s performance for a given feature; and 2) a linear classification scheme to arrange the results obtained in step 1. Based on these results QM4VR selects the most appropriate subflows for data delivery in order to achieve improvement of VR QOS levels

An innovative algorithm for improved quality multipath delivery of Virtual Reality content

This paper describes and evaluates an Innovative Algorithm for Improved Quality Multipath Delivery of Virtual Reality Content (QM4VR) that addresses the stringent communication requirements of Virtual Reality (VR) applications. Making use of the Multipath TCP (MPTCP) built-in multipath delivery features (subflows), QM4VR explores the subflows' characteristics, evaluates their performance (e.g., delay, throughput or loss) and proposes a new management scheme to improve the Quality of Service (QOS) of the VR applications. glsqm4vr adopts a Machine Learning (ML)-based approach to evaluate the subflows' performance which is implemented in two steps: 1) a linear regression scheme to forecast the subflow's performance for a given feature; and 2) a linear classification scheme to arrange the results obtained in step 1. Based on these results QM4VR selects the most appropriate subflows for data delivery in order to achieve improvement of VR QOS levels

Performance Analysis of an IoT Platform with Virtual Reality and Social Media Integration

The Internet of Things (IoT) is a growing network of physical objects where the devices are connected to the Internet through unique addressing schemes and multiple protocols. The increase of IoT devices in the recent years presents significant challenges in terms of security, authentication and usability. The recently introduced Social Internet of Things (SIoT) tries to address these challenges with the virtualisation of IoT devices and the use of an infrastructure where people and IoT devices can communicate with each other, both in the real-world and virtual-world, through a common platform. In the proposed SIoT architecture, IoT devices can be operated by virtual reality (VR)headsets and Twitter, a social media platform. The aim of the platform is to allow users to seamlessly operate IoT devices, using their preferred interface: remotely with text messages (i.e. tweets) and VR headsets or operate the IoT devices directly. This paper also describes the implementation of a testbed and presents the performance analysis of the solution, demonstrating its feasibility and low latency

Performance evaluation of a multi-user virtual reality platform

Virtual Reality (VR) popularity is increasing as it is becoming more affordable for end users. Available VR hardware includes low-end inexpensive devices like Google Cardboard and high-end ones like HTC Vive or Oculus Rift, which are more expensive headsets. Using VR as a platform for content delivery allows better user engagement than other traditional methods, as VR headsets remove external distractions. Multi- user VR applications provide shared experiences where users can communicate and interact in the same virtual space. This shared environment, however, introduces challenges regarding network performance, quality of service (QoS) and sessions privacy. This paper presents a multi-user VR application and aims to evaluate network behaviour in a number of scenarios, including real VR headsets (i.e. Oculus Rift), as well as simulated ones. This QoS analysis is important for the understanding of how many VR users can be simultaneously connected with high image qualit

AVIRA: Enhanced multipath for content-aware adaptive virtual reality

This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows’ performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a "low-level" application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient

AVIRA: Enhanced multipath for content-aware adaptive Virtual Reality

This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows' performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a 'low-level' application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective

Intelligent Immersion: The current landscape of AI tools in the Mixed Reality development pipeline for creative experiences

Among the technologies that hold immense potential to revolutionize how we interact with information and digital content, Mixed Reality (MR) offers unique immersive experiences that seamlessly integrate virtual objects into the user's physical environment. This groundbreaking fusion of the physical and digital worlds has a complex development process which is a fertile ground for applications of Artificial Intelligence (AI). This article aims to provide a comprehensive overview of AI tools and their applications, in all stages of the development of MR experiences for the creative sector. It also discusses the challenges and opportunities of incorporating them in the development pipeline and offer some use guidelines as a compass to navigate this rapidly changing landscape.Arts and Humanities Research Council (UK, AH/W005530/1) and National Endowment For the Humanities (US, HND-284975-22