Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Q-FDBA
T2 - improving QoE fairness for video streaming.
AU - Jiang, Jingyan
AU - Hu, Liang
AU - Hao, Pingting
AU - Sun, Rui
AU - Hu, Jiejun
AU - Li, Hongtu
PY - 2018/5/31
Y1 - 2018/5/31
N2 - Multiplayer video streaming scenario can be seen everywhere today as the video traffic is becoming the “killer” traffic over the Internet. The Quality of Experience fairness is critical for not only the users but also the content providers and ISP. Consequently, a QoE fairness adaptive method of multiplayer video streaming is of great importance. Previous studies focus on client-side solutions without network global view or network-assisted solution with extra reaction to client. In this paper, a pure network-based architecture using SDN is designed for monitoring network global performance information. With the flexible programming and network mastery capacity of SDN, we propose an online Q-learning-based dynamic bandwidth allocation algorithm Q-FDBA with the goal of QoE fairness. The results show the Q-FDBA could adaptively react to high frequency of bottleneck bandwidth switches and achieve better QoE fairness within a certain time dimension.
AB - Multiplayer video streaming scenario can be seen everywhere today as the video traffic is becoming the “killer” traffic over the Internet. The Quality of Experience fairness is critical for not only the users but also the content providers and ISP. Consequently, a QoE fairness adaptive method of multiplayer video streaming is of great importance. Previous studies focus on client-side solutions without network global view or network-assisted solution with extra reaction to client. In this paper, a pure network-based architecture using SDN is designed for monitoring network global performance information. With the flexible programming and network mastery capacity of SDN, we propose an online Q-learning-based dynamic bandwidth allocation algorithm Q-FDBA with the goal of QoE fairness. The results show the Q-FDBA could adaptively react to high frequency of bottleneck bandwidth switches and achieve better QoE fairness within a certain time dimension.
U2 - 10.1007/S11042-017-4917-1
DO - 10.1007/S11042-017-4917-1
M3 - Journal article
VL - 77
SP - 10787
EP - 10806
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 9
ER -