Real-time applications often have mixed hard and soft deadlines, can be preempted subject to the cost of context switching or the restart of computation, and have various data dependency. The simple but widely used task completion ratio, as the Quality of Service (QoS) metric, does not capture these characteristics and can not reflect user perceived QoS well. In this paper, we propose a new quantitative QoS metric, which is based on task completion ratio but differentiates hard and soft deadlines and models data dependency as well. Basically, it assigns different weights to hard and soft deadline tasks, penalizes late soft task completion, and measures the tasks affected by any dropped tasks. We apply popular online schedulers, such as EDF (earliest deadline first), FCFS (first come first serve), and LETF (least execution time first), on a set of simulated MPEG movies at the frame level and for each application compare the new QoS measurement, traditional completion ratio with the “real” completion ratio which considers the number of correctly decoded frames and has been mapped to the user perceived QoS well. Experimental results show that our proposed QoS metric can reflect real life QoS much better than the traditional one.