User Quality of Experience-aware Multimedia Streaming over Wireless Home Area Network

For multimedia streaming over wireless networks, there is a trade-off between the capacity of the wireless links and the end-user perceived-quality, which can be affected by the compression scheme used, content characteristics and adaptation algorithm (if any). In this paper, this trade-off is investigated for streaming various motion content multimedia over an IEEE 802.11b-based Wireless-Home Area Network using the Quality-Oriented Adaptation Scheme (QOAS). QOAS performance is compared to that of a non-adaptive scheme when using MPEG-2 and MPEG-4 encoding in terms of average end-user perceived quality, number of streaming sessions concurrently supported, loss rate, delay, jitter and total throughput. Simulation results show that by using QOAS and MPEG-4 encoded streams a much higher number of concurrent streams are supported at an average quality above “good” level on the ITU-T five-point quality scale in comparison with other situations. In this case all the other streaming performance parameters were also significantly better

Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks

Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to users’ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability

Quality-oriented adaptation scheme for multimedia streaming in local broadband multi-service IP networks

The research reported in this thesis proposes, designs and tests the Quality-Oriented Adaptation Scheme (QOAS), an application-level adaptive scheme that offers high quality multimedia services to home residences and business premises via local broadband IP-networks in the presence of other traffic of different types. QOAS uses a novel client-located grading scheme that maps some network-related parameters’ values, variations and variation patterns (e.g. delay, jitter, loss rate) to application-level scores that describe the quality of delivery. This grading scheme also involves an objective metric that estimates the end-user perceived quality, increasing its effectiveness. A server-located arbiter takes content and rate adaptation decisions based on these quality scores, which is the only information sent via feedback by the clients. QOAS has been modelled, implemented and tested through simulations and an instantiation of it has been realized in a prototype system. The performance was assessed in terms of estimated end-user perceived quality, network utilisation, loss rate and number of customers served by a fixed infrastructure. The influence of variations in the parameters used by QOAS and of the networkrelated characteristics was studied. The scheme’s adaptive reaction was tested with background traffic of different type, size and variation patterns and in the presence of concurrent multimedia streaming processes subject to user-interactions. The results show that the performance of QOAS was very close to that of an ideal adaptive scheme. In comparison with other adaptive schemes QOAS allows for a significant increase in the number of simultaneous users while maintaining a good end-user perceived quality. These results are verified by a set of subjective tests that have been performed on viewers using a prototype system

OFLoad: An OpenFlow-based dynamic load balancing strategy for datacenter networks

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-datacenters, meant to deal with this explosion of data traffic. However this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators’ revenue. In this context there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for datacenter networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity

Una excursió per muntanyes i valls : Gerdhard Ertl i la química de superfícies

El Nobel de Química del 2007 va premiar el treball de Gerdhard Ertl sobre química de superfícies. S'exposen alguns dels seus resultats i com van ser possibles, així com la seva aplicació en catàlisis heterogènies de processos importants

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

A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes

A deep reinforcement learning-based offloading scheme for multi-access edge computing-supported eXtended reality systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption