Scalable coding is potentially useful in content distribution over unreliable channels, as it enables meaningful reconstruction when the hierarchical bitstream is only partially received. However, its deployment in conjunction with predictive coding may result in considerable performance degradation when errors due to packet loss propagate through the prediction loop. Despite this, most existing predictive scalable coding techniques employ components whose design completely ignores the effects of unreliable channel conditions. This paper proposes an efficient design technique for predictive scalable coding systems, which effectively accounts for: i) all available information at a given layer by optimizing its prediction parameters within an estimation-theoretic framework; ii) the uncertainty due to packet loss via estimation and minimization of end-to-end distortion. It further leverages an asymptotic closed loop design technique for the predictor and quantizer modules, which provides the stability benefits of open-loop design, while ultimately optimizing the system for closed-loop operation. Experimental results provide compelling evidence for the effectiveness of the approach, with considerable performance gains over existing design techniques, in terms of end-to-end signal-to-noise ratio.