QoE-aware Uplink Resource Allocation Scheme for Live Streaming in Mobile Edge Computing

• Main Authors: Tsung-Sheng,Chen, 陳宗昇
• Other Authors: Ren-Hung,Hwang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/a6uz2z

碩士 === 國立中正大學 === 資訊工程研究所 === 106 === The traffic of Internet service increase each year, and video is the largest. With the development of video streaming, live streaming is gradually emerging, and the growing popularity of mobile device, more and more live streaming source is from mobile devices, which also causes congestion. Therefore, ETSI propose Mobile edge computing, which is 5G-based evolution in 2014, improve the current network architecture, and make user have better user experience and saved bandwidth . In this paper we focus on end user who upload live streaming with radio resource allocation problem. In the past, radio resources are allocated in the continuous band . However, since the uplink traffic of the mobile network increases, the continuous bandwidth limits the flexibility of radio resources allocation. Therefore, the carrier aggregation technology is proposed in 3GPP Rel-10, which can aggregates some segments to achieve the required bandwidth. In order to increase the entire bandwidth, this paper will also use carrier aggregation. In Uplink, Single Carrier Frequency division multiple access has lower transmission speed than OFDMA, but it has better power efficiency. Because the power is limited, SC-FDMA is better than OFDMA. Most of the uplink radio resource scheduling only consider channel conditions to allocate resource block, but doesn’t consider the requirement of UE, it causing the waste of resources. In addition, for the live streaming, the minimum quality of service is guaranteed, but it doesn’t guarantee the good user experience for the viewer. Therefore, how to make all UE have better QoE in the limited resources is the most important. This paper proposes a design of QoE-aware resource scheduler based on the Max-min algorithm to allocate resource blocks for each UE, compare all UE QoE and fairness with the other scheduler.