Towards an LTE hybrid unicast broadcast content delivery framework

The era of ubiquitous access to a rich selection of interactive and high quality multimedia has begun; with it, significant challenges in data demand have been placed on mobile network technologies. Content creators and broadcasters alike have embraced the additional capabilities offered by network delivery; diversifying content offerings and providing viewers with far greater choice. Mobile broadcast services introduced as part of the Long Term Evolution (LTE) standard, that are to be further enhanced with the release of 5G, do aid in spectrally efficient delivery of popular live multimedia to many mobile devices, but, ultimately rely on all users expressing interest in the same single stream. The research presented herein explores the development of a standards aligned, multi-stream aware framework; allowing mobile network operators the efficiency gains of broadcast whilst continuing to offer personalised experiences to subscribers. An open source, system level simulation platform is extended to support broadcast, characterised and validated. This is followed by the implementation of a Hybrid Unicast Broadcast Synchronisation (HUBS) framework able to dynamically vary broadcast resource allocation. The HUBS framework is then further expanded to make use of scalable video content

Adaptive subframe allocation for next generation multimedia delivery over hybrid LTE unicast broadcast

The continued global roll-out of long term evolution (LTE) networks is providing mobile users with perpetually increasing ubiquitous access to a rich selection of high quality multimedia. Interactive viewing experiences including 3-D or free-viewpoint video require the synchronous delivery of multiple video streams. This paper presents a novel hybrid unicast broadcast synchronisation (HUBS) framework to synchronously deliver multi-stream content. Previous techniques on hybrid LTE implementations include staggered modulation and coding scheme grouping, adaptive modulation coding or implementing error recover techniques; the work presented here instead focuses on dynamic allocation of resources between unicast and broadcast, improving stream synchronisation as well as overall cell resource usage. Furthermore, the HUBS framework has been developed to work within the limitations imposed by the LTE specification. Performance evaluation of the framework is performed through the simulation of probable future scenarios, where a popular live event is broadcast with stereo 3-D or multi-angle companion views interactively offered to capable users. The proposed framework forms a ``HUBS group'' that monitors the radio bearer queues to establish a time lead or lag between broadcast and unicast streams. Since unicast and broadcast share the same radio resources, the number of subframes allocated to the broadcast transmission are then dynamically increased or decreased to minimise the average lead/lag time offset between the streams. Dynamic allocation showed improvements for all services across the cell, whilst keeping streams synchronised despite increased user loading

Radio frequency traffic classification over WLAN

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types

Performance evaluation of 3D-TV transmission over WiMAX broadband access networks

3-Dimensional Television (3D-TV) is an emerging area of consumer entertainment, which gives its viewers an added dimension of experience. At the moment 3DTV is going through a series of standardization activities, especially to finalize a representation technique. Extra information that needs to be transmitted in 3D-TV systems has brought about new problems that need to be solved. Models have been introduced to explain the distortion characteristics in traditional 2D video, when they are transmitted over existing wireless networks. However, the effect of transmission errors caused by bandwidth limited wireless channels on 3D video is not yet fully known. In this paper we present a performance evaluation of 3D-TV transmission over an error prone WiMAX broadband wireless access network. Experiments are performed on two popular types of representation methods of 3D video, namely color plus depth and left and right representation. Based on these experiments, important conclusions regarding 3D-TV transmission over WiMAX networks are presented