IP-networked streaming media storage has been increasingly used as a part of many applications. Random placement of data blocks has been proven to be an effective approach to balance heterogeneous workload in multi-disk steaming architectures. However, the main disadvantage of this technique is that statistical variation can still result in short term load imbalances in disk utilization. We propose a packet level randomization (PLR) technique to solve this challenge. We quantify the exact performance trade-off between PLR approach and the traditional block level randomization (BLR) technique through both theoretical analysis and extensive simulation. Our results show that the PLR technique can achieve much better load balancing in scalable streaming architectures by using more memory space.