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In a recent work, the authors proposed a novel paradigm for interactive video streaming and coined the term JPEG2000-Based Scalable Interactive Video (JSIV) for it. In this work, we investigate JSIV when motion compensation is employed to improve prediction, something that was intentionally left out in our earlier treatment. JSIV relies on three concepts: storing the video sequence as independent JPEG2000 frames to provide quality and spatial resolution scalability, prediction and conditional replenishment of code-blocks to exploit inter-frame redundancy, and loosely coupled server and client policies in which a server optimally selects the number of quality layers for each code-block transmitted and a client makes the most of the received (distorted) frames. In JSIV, the server transmission problem is optimally solved using Lagrangian-style rate-distortion optimization. The flexibility of JSIV enables us to employ a wide variety of frame prediction arrangements, including hierarchical B-frames. JSIV provides considerably better interactivity compared with existing schemes and can adapt immediately to interactive changes in client interests, such as forward or backward playback and zooming into individual frames. Experimental results show that JSIV's performance is inferior to that of SVC in conventional streaming applications while JSIV performs better in interactive browsing applications. [ABSTRACT FROM PUBLISHER]