Quality of Experience-Aware Progressive Video Caching and Adaptive Device-to-Device Video Streaming for Cellular Networks with High User Density

Abstract

The increasing popularity of video streaming is escalating the growth of data traffic over cellular networks. Consequently, new techniques are much needed to help serving this increasing video traffic. Furthermore, the new techniques should consider the complex, dynamic, and delay-sensitive nature of video streaming traffic to support good Quality of Experience (QoE) video streaming services over cellular networks. The work in this thesis is focused on proposing algorithms and techniques to enhance the delivery of video contents and to improve the QoE of video streaming over cellular networks with high user density. First, we propose two algorithms for progressive caching of video segments in User Equipments (UEs) and Device-to-Device (D2D) transmission of video contents among UEs in the cell. The algorithms are employed, by the Base-Station (BS), to send segments of video files to selected UEs in the cellular network (called Storage Members (SMs)), to cache and forward the segments to requesting UEs using D2D communication. We study the performance of both algorithms in terms of the hit ratio as well as the achieved data rates. The parameters of the wireless communication on the Radio Access Network (RAN) between the BS and UEs have an effect on video streaming QoE. As such, we analyze the impact of the wireless transmission parameters in Long Term Evolution-Advanced (LTE-A) networks on video streaming QoE. We consider both cell-level and link-level parameters. Moreover, we propose an architecture for improving the QoE of video streaming in cellular networks with high user density. The architecture employs the aforementioned algorithms. Furthermore, the architecture employs Dynamic Adaptive Streaming over HTTP (DASH); an adaptive video streaming technique. We study the improvements achieved by the proposed architecture in terms of many video streaming QoE metrics. Thereafter, we improve the operation of the proposed architecture by introducing QoE awareness to both caching and distribution of video segments. We employ QoE awareness in three aspects of the proposed architecture; cellular resource allocation, caching of video segments, and SM assignment optimization. We analyze the improvements achieved by each QoE awareness technique in terms of video streaming QoE metrics.