TY - GEN
T1 - QDLCoding
T2 - 2017 IEEE Conference on Computer Communications, INFOCOM 2017
AU - Gao, Guanyu
AU - Wen, Yonggang
AU - Hu, Han
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - Adaptive bitrate (ABR) streaming is the de facto solution in online video services to cope with heterogeneous devices and varying network connections. However, this solution is computation intensive, demanding a large number of servers for encoding videos. Moreover, due to the time-varying nature of video generation, intelligent strategies are required in order to determine the right amount of resources for encoding. The situation is further complicated by the fact that, the two types of co-existing video content, live content and Video-on-Demand (VoD) content, have different QoS requirements for encoding. These observations posit daunting challenges for meeting the heterogeneous QoS requirements with a minimum computing capacity. This paper proposes the QoS-differentiated low-cost video encoding (QDLCoding) scheme to address these challenges. We develop a framework for scheduling the encoding workloads of the two types of videos with statistical QoS guarantees. Each type of videos is specified with a QoS criterion and a QoS loss bound. The objective is to provision the minimum amount of resources while keeping the QoS loss probabilities within the prescribed bounds. We design an online algorithm that can determine the minimum required capacity by learning content arrival distributions. The experiment results demonstrate that our method can greatly reduce the required capacity for encoding online videos while controlling the likelihood of QoS loss precisely.
AB - Adaptive bitrate (ABR) streaming is the de facto solution in online video services to cope with heterogeneous devices and varying network connections. However, this solution is computation intensive, demanding a large number of servers for encoding videos. Moreover, due to the time-varying nature of video generation, intelligent strategies are required in order to determine the right amount of resources for encoding. The situation is further complicated by the fact that, the two types of co-existing video content, live content and Video-on-Demand (VoD) content, have different QoS requirements for encoding. These observations posit daunting challenges for meeting the heterogeneous QoS requirements with a minimum computing capacity. This paper proposes the QoS-differentiated low-cost video encoding (QDLCoding) scheme to address these challenges. We develop a framework for scheduling the encoding workloads of the two types of videos with statistical QoS guarantees. Each type of videos is specified with a QoS criterion and a QoS loss bound. The objective is to provision the minimum amount of resources while keeping the QoS loss probabilities within the prescribed bounds. We design an online algorithm that can determine the minimum required capacity by learning content arrival distributions. The experiment results demonstrate that our method can greatly reduce the required capacity for encoding online videos while controlling the likelihood of QoS loss precisely.
UR - https://www.scopus.com/pages/publications/85034021212
U2 - 10.1109/INFOCOM.2017.8057024
DO - 10.1109/INFOCOM.2017.8057024
M3 - Conference contribution
AN - SCOPUS:85034021212
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2017 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 May 2017 through 4 May 2017
ER -