%0 Conference Paper %1 info3-inproceedings-2013-459 %A Hoßfeld, Tobias %A Strohmeier, Dominik %A Raake, Alexander %A Schatz, Raimund %B 5th ACM Workshop on Mobile Video (MoVid 2013) %C Oslo, Norway %D 2013 %K myown demobenefit oekonet smartenit %T Pippi Longstocking Calculus for Temporal Stimuli Pattern on YouTube QoE %X Video streaming over wireless broadband is currently re- sponsible for half of the world-wide mobile data traffic and its share is expected to even further increase. Therefore, mobile operators need to understand how Quality of Ex- perience (QoE) of mobile video streaming is impacted by through the network delivery. Due to HTTP video stream- ing used by video portals like YouTube or NetFlix, problems or insufficient resources in the networks manifest as stalling events during service consumption on an end user device. In this paper, we discuss the challenges for QoE monitoring for HTTP-based mobile video regarding three aspects. First, the impact of different patterns in terms of stalling event frequency and length on QoE is quantified on behalf of a study on YouTube. Second, we evaluate predictive power of aggregate measures like total stalling time. Third, we discuss the resulting consequences and challenges for mobile video QoE modeling, monitoring and provisioning.

@inproceedings{info3-inproceedings-2013-459, abstract = {Video streaming over wireless broadband is currently re- sponsible for half of the world-wide mobile data traffic and its share is expected to even further increase. Therefore, mobile operators need to understand how Quality of Ex- perience (QoE) of mobile video streaming is impacted by through the network delivery. Due to HTTP video stream- ing used by video portals like YouTube or NetFlix, problems or insufficient resources in the networks manifest as stalling events during service consumption on an end user device. In this paper, we discuss the challenges for QoE monitoring for HTTP-based mobile video regarding three aspects. First, the impact of different patterns in terms of stalling event frequency and length on QoE is quantified on behalf of a study on YouTube. Second, we evaluate predictive power of aggregate measures like total stalling time. Third, we discuss the resulting consequences and challenges for mobile video QoE modeling, monitoring and provisioning.}, added-at = {2016-03-14T16:41:12.000+0100}, address = {Oslo, Norway}, author = {Hoßfeld, Tobias and Strohmeier, Dominik and Raake, Alexander and Schatz, Raimund}, biburl = {https://www.bibsonomy.org/bibtex/2e2ac05f7f675e81a242e0c3f47dc01bb/uniwue_info3}, booktitle = {5th ACM Workshop on Mobile Video (MoVid 2013)}, interhash = {ff7819602d6e2261e0d1afe23a9f8891}, intrahash = {e2ac05f7f675e81a242e0c3f47dc01bb}, keywords = {myown demobenefit oekonet smartenit}, month = {2}, timestamp = {2022-03-14T00:10:56.000+0100}, title = {Pippi Longstocking Calculus for Temporal Stimuli Pattern on YouTube QoE}, year = 2013 }