We consider video processing to detect complex activities in a distributed network consisting of mobile devices and video-cloud servers. To address varying task requirements and resource-constraints of mobile devices, we consider fragmentation of the video processing workflow. Fragmentation of the workflow allows for the mobile device to filter video clips based on metadata, process portions of the clips, and offload other videos to video-cloud servers. In certain situations, the cloud may require access to the raw video even if the video is processed by a mobile device. Through the use of resizing the image and a top-k analysis, we explore various quality of information metrics in the addressing of complex activity detection that consider energy usage of the mobile devices and the quality of detecting objects in terms of completeness and timeliness.