Cross-layer design for multi-view video plus depth transmission over LTE networks in crowd event scenarios

Abstract

The development of digital multimedia systems has seen an unprecedented growth in recent years, with immersive video technology taking a central role. An application which is driving interest in this technology is free-viewpoint video which allows viewers to interactively navigate a scene by selecting their preferred viewing position. This is made possible through the generation of novel viewpoints rendered from a small set of texture and depth map views using a view synthesis technique. Meanwhile, according to Cisco, mobile video traffic accounted for 60% of total mobile data traffic in 2016 and this is expected to reach 78% by 2021. This growth in mobile video traffic coupled with the introduction of free-viewpoint video in the mobile ecosystem will have an impact on the user experience especially in crowd event scenarios. Such scenarios are characterised by high uplink user data traffic coupled with excessive uplink signalling overhead caused by channel quality feedback reports. In this thesis, the high uplink signalling overhead problem is tackled through the design and development of a set of novel Channel Quality Indicator (CQI) feedback reduction schemes. These are based on a User Equipment (UE)-assisted predictive filtering technique and a CQI clustering scheme respectively, where the latter is able to achieve an uplink signalling feedback reduction of 88.2%. Moreover, a cross-layer depth-texture bit rate allocation estimation technique and an enhanced depth map rate control scheme aimed at improving the synthesised view quality is proposed. Furthermore, a content-aware scheduling algorithm based on the widely used modified largest weighted delay first (M-LWDF) packet scheduling scheme is designed and tested in conjunction with the combined CQI feedback reduction schemes mentioned above. Whilst, the content-aware scheduling scheme yields an improvement in both the system performance and visual quality metrics, the use of the feedback reduction schemes has a detrimental effect on the visual quality. For this reason, a lean cross layer technique is designed to adapt the CQI feedback by soliciting CQI reports from individual UEs. This solution has not only improved the texture and synthesised view Peak Signal-to-Noise Ratio (PSNR) quality, approaching that of the content-aware MLWDF scheme without any CQI feedback reduction applied, but also achieves an uplink feedback signalling overhead reduction of 84.1%.