Jose Oscar Fajardo

Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

Video Quality Prediction Model for H.264 Video over UMTS Networks and Their Application in Mobile Video Streaming

Universal Mobile Telecommunication System (UMTS) is a third generation mobile communication systems that supports wireless wideband multimedia applications. The objective of this paper is to present a new model for non-intrusive prediction of H.264 encoded video quality over UMTS networks and to illustrate their application to video quality monitoring and adaptation in mobile wireless streaming services. First, we present an efficient regression model for predicting video quality non-intrusively for all content types. The model is predicted from a combination of a set of objective parameters in the application and physical layer in terms of the Mean opinion Score (MOS). The application layer parameters considered are the content type, sender bitrate and frame rate and the physical layer parameters are the block error rate modeled with 2-state Markov model for a mean burst length of 1.75. The performance of the proposed metric is evaluated with unseen dataset with good prediction accuracy. Second, we illustrate the application of the model in mobile streaming services by presenting a new Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven. The scheme was tested and evaluated in the NS2 based UMTS simulation network. Extensive simulation results demonstrate the effectiveness of the proposed adaptation scheme in terms of the MOS and especially at the UMTS network bottleneck access where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

Introducing Mobile Edge Computing Capabilities through Distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

Technology pillars in the architecture of future 5G mobile networks: NFV, MEC and SDN

This paper analyzes current standardization situation of 5G and the role network softwarization plays in order to address the challenges the new generation of mobile networks must face. This paper surveys recent documentation from the main stakeholders to pick out the use cases, scenarios and emerging vertical sectors that will be enabled by 5G technologies, and to identify future high-level service requirements. Driven by those service requirements 5G systems will support diverse radio access technology scenarios, meet end-to-end user experienced requirements and provide capability of flexible network deployment and efficient operations. Then, based on the identified requirements, the paper overviews the main 5G technology trends and design principles to address them. In particular, the paper emphasizes the role played by three main technologies, namely SDN, NFV and MEC, and analyzes the main open issues of these technologies in relation to 5G.

Improving content delivery efficiency through multi-layer mobile edge adaptation

This article presents a novel architecture for optimizing HTTP-based multimedia delivery in multi-user mobile networks. This proposal combines the usual client-driven dynamic adaptation scheme DASH-3GPP with network-assisted adaptation capabilities, in order to maximize the overall quality of experience. The foundation of this combined adaptation scheme is based on two state of the art technologies. On one hand, adaptive HTTP streaming with multi-layer encoding allows efficient media delivery and improves the experienced media quality in highly dynamic channels. Additionally, it makes it possible to implement network-level adaptations for better coping with multi-user scenarios. On the other hand, mobile edge computing facilitates the deployment of mobile services close to the user. This approach brings new possibilities in modern and future mobile networks, such as close to zero delays and awareness of the radio status. The proposal in this article introduces a novel element, called the mobile edge-DASH adaptation function, which combines all these advantages to support efficient media delivery in mobile multi-user scenarios. Furthermore, we evaluate the performance enhancements of this content-aware and user context-aware scheme through simulations of a mobile multimedia scenario.

QoE-aware optimization of multimedia flow scheduling

This paper deals with the subjective quality maximization problem when scheduling multimedia traffic flows in a shared channel. In order to quantify such user satisfaction a utility function dependent on flow transfer delay is used. In this context, we formulate the Quality of Experience aware resource allocation optimization problem as a Markov Decision Process. This model is analytically unsolvable in general, and as an approximate solution we develop a simple and tractable index rule based on Gittins approach, originally aimed just at minimizing mean flow delay. As concluded from simulation results, when evaluating subjective quality performance this novel index rule proposal outperforms the most relevant existing scheduling disciplines.

QoE-driven dynamic management proposals for 3G VoIP services

Most of the currently available adaptation solutions of VoIP over UMTS are based on the modification of service parameters as the only available reaction against any detected service degradation. On the contrary, in this paper we propose a combined approach where service-level adaptation is considered first and, provided that no suitable parameter combination is capable of providing enough QoE, a change of network state will be suggested. In order to do so we analyze the performance of the end-to-end (e2e) performance metrics in this convergent scenario, the root causes of possible degradations and, finally, the combined effects of the different network segments and their impact on the user perceived QoE. We show the map of best performing VoIP configurations for every state of the network segments. Furthermore, considering each of these configurations, we analyze the acceptability of the service or the convenience of trying to modify the network state. Finally, a lightweight implementation based on simple network state estimation and decision heuristics is proposed and validated in terms of accuracy and responsiveness.

Impact of end devices on subjective video quality assessment for QCIF video sequences

The aim of this paper is to assess the impact of devices on Quality of Experience (QoE) of the user. As the types of video content viewed over handheld devices increases e.g. streaming video, it is important to evaluate the impact of the end device on video quality assessment. In this paper subjective tests were conducted with QCIF (176×144) videos over two devices — Personal Computer (PC) and mobile handset with 90 test conditions. The dataset was generated with a combination of parameters associated with the access network (block error rates and mean burst lengths), H.264 codec related parameter (sender bitrates) and content types. Subjects favoured PC test over handset test in almost all test scenarios. These studies should help in context-aware services in deciding the appropriate encoding parameters based on type of device being used to receive content.

QoE-Oriented Mobile Edge Service Management Leveraging SDN and NFV

5G envisages a “hyperconnected society” where trillions of diverse entities could communicate with each other anywhere and at any time, some of which will demand extremely challenging performance requirements such as submillisecond low latency. Mobile Edge Computing (MEC) concept where application computing resources are deployed at the edge of the mobile network in proximity of an end user is a promising solution to improve quality of online experience. To make MEC more flexible and cost-effective Network Functions Virtualisation (NFV) and Software-Defined Networking (SDN) technologies are widely adopted. It leads to significant CAPEX and OPEX reduction with the help of a joint radio-cloud management and orchestration logic. In this paper we discuss and develop a reference architecture for the orchestration and management of the MEC ecosystem. Along with the lifecycle management flows of MEC services, indicating the interactions among the functional modules inside the Orchestrator and with external elements, QoS management with a focus on the channel state information technique is presented.

Enabling technologies and benefits of multi-tenant multi-service 5G small cells

In this manuscript, we present the main concepts of the 5G-PPP SESAME project dedicated to the implementation of Cloud-Enabled Small Cells (CESCs), able to support edge cloud computing in a multi-tenant, multi-service ecosystem. More particularly, we give a preview of the SESAME concept at the component/sub-system, system and operation level. At the component/sub-system level, we detail our plan to deploy multi-operator enabled small cells, enhanced with a virtualised execution platform for 5G. At the system level, we present the envisaged architecture to manage and control the cloud-enabled small cell infrastructure. Finally, at the operation level, we explain the potential advantages of adopting the SESAME concept on the 5G access networks.