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For video or web services, it is crucial to measure user-perceived quality ofexperience (QoE) at scale under various video quality or page loading delays.However, fast QoE measurements remain challenging as they must elicitsubjective assessment from human users. Previous work either (1) automates QoEmeasurements by letting crowdsourcing raters watch and rate QoE test videos or(2) dynamically prunes redundant QoE tests based on previously collected QoEmeasurements. Unfortunately, it is hard to combine both ideas becausetraditional crowdsourcing requires QoE test videos to be pre-determined beforea crowdsourcing campaign begins. Thus, if researchers want to dynamically pruneredundant test videos based on other test videos' QoE, they are forced tolaunch multiple crowdsourcing campaigns, causing extra overheads tore-calibrate or train raters every time.  This paper presents VidPlat, the first open-source tool for fast andautomated QoE measurements, by allowing dynamic pruning of QoE test videoswithin a single crowdsourcing task. VidPlat creates an indirect shim layerbetween researchers and the crowdsourcing platforms. It allows researchers todefine a logic that dynamically determines which new test videos need more QoEratings based on the latest QoE measurements, and it then redirectscrowdsourcing raters to watch QoE test videos dynamically selected by thislogic. Other than having fewer crowdsourcing campaigns, VidPlat also reducesthe total number of QoE ratings by dynamically deciding when enough ratings aregathered for each test video. It is an open-source platform that futureresearchers can reuse and customize. We have used VidPlat in three projects(web loading, on-demand video, and online gaming). We show that VidPlat canreduce crowdsourcing cost by 31.8% - 46.0% and latency by 50.9% - 68.8%.

VidPlat: A Tool for Fast Crowdsourcing of Quality-of-Experience Measurements