In this paper, we study the data caching problem in mobile cloud environment where multiple correlated data items could be packed and migrated to serve a predefined sequence of requests. By leveraging the spatial and temporal trajectory of requests, we propose a two-phase caching algorithm. We first investigate the correlation between data items to determine whether or not two data items could be packed to transfer, and then combine an existing dynamic programming (DP)-based algorithm and a greedy strategy to design a two-phase algorithm, named DP_Greedy, for effectively caching these shared data items to serve a predefined sequence of requests. Under homogeneous cost model, we prove the proposed algorithm is at most 2/α times worse than the optimal one in terms of the total service cost, where α is the defined discount factor, and also show that the algorithm can achieve this results within O(mn) time and O(mn) space complexity for m caches to serve a n-length sequence. We evaluate our algorithm by effectively implementing it and comparing it with the non-packing case, the result show the proposed DP_Greedy algorithm not only presents excellent performances but is also more in line with the actual situation.