Current studentsExisting staffContact us
 

Modeling buffer starvations of video streaming in cellular networks with large-scale measurement of user behavior

Simple item page
dc.contributor.authorXu, Y.
dc.contributor.authorXiao, Z.
dc.contributor.authorFeng, H.
dc.contributor.authorYang, T.
dc.contributor.authorHu, B.
dc.contributor.authorZhou, Y.
dc.date.issued2017
dc.description.abstractUnraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the s tarvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the 'least' information from content providers.
dc.identifier.citationIEEE Transactions on Mobile Computing, 2017; 16(8):2228-2245
dc.identifier.doi10.1109/TMC.2016.2616402
dc.identifier.issn1536-1233
dc.identifier.urihttps://hdl.handle.net/11541.2/128718
dc.language.isoen
dc.publisherIEEE
dc.relation.fundingNatural Science Foundation of China 61402114
dc.relation.fundingShanghai Pujiang Program 14PJ1401400
dc.relation.fundingHuawei Innovative Research Program HIRPO20140112
dc.relation.fundingFudan Zhuoxue Program
dc.relation.fundingOpen project of State Key Laboratory for Novel Software Technology, Nanjing University KFKT2016B01
dc.relation.fundingEU FP7 IRSES MobileCloud project 612212
dc.rightsCopyright 2016 IEEE Access Condition Notes: Post print available on open access
dc.source.urihttps://doi.org/10.1109/TMC.2016.2616402
dc.subjectmeasurement
dc.subjectquality of experience
dc.subjectbuffer starvation
dc.subjectordinary differential equations
dc.subjectdiscriminatory processor sharing
dc.subjectBayesian inference
dc.titleModeling buffer starvations of video streaming in cellular networks with large-scale measurement of user behavior
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12149799900001831 13196808460001831 9916153111501831_12149799900001831_13149779900001831_CS
ror.mmsid9916153111501831

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
9916153111501831_12149799900001831_13149779900001831_CS.pdf
Size:
3.31 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections