A new model-based scheme for clock offset estimationbetween two nodes is presented.The one-way delay is modeled with a gamma distribution representing the actual state of the network link.Real and modeled (with zero shift) one-way delay measurements are QQ-plot for the estimation of the clock offset.The proposed model is non-intrusive (the kernel is not modified), and can be implemented at the application layer.The model provides accuracy in the order of 3-5 milliseconds under different traffic conditions. Having in mind multimedia systems applications, we propose a novel model-based approach to estimate the clock-offset between two nodes on the Internet. Different than current clock-offset schemes in the literature, which are iterative in nature, our scheme is aimed at getting a good non-iterative clock-offset estimation in real time (in the order of milliseconds). In our clock-offset estimation approach, the One-Way Delay (OWD) measurements are modeled with a shifted gamma distribution representing the current state of the probing link. By using the QQ-probability plot technique and linear regression model, we estimate the (shift parameter or) minimum value of the gamma distribution with probability zero. This estimated value represents the clock offset plus network propagation and transmission delay (queuing delay has already been eliminated) for the corresponding receiving path. End nodes exchange their corresponding minimum estimates and get an improved final clock offset estimate considering the network path asymmetries. Based on real experiments, we show that our scheme provides an extremely fast clock-offset estimation with lower RMSE and superior stability than NTP and current NTP-like state of the art methodologies in the literature Jeske and Sampath (2003), Choi and Yoo (2005), Adhikari et al. (2003), Tsuru et al. (2002). Moreover, our proposed scheme is non-intrusive (no kernel programming needed), easy to implement, and targeted as part of more complex real-time multimedia distribution protocols requiring a fast and reliable OWD estimates.