Wireless data broadcast is an efficient way to disseminate digital information to clients equipped with mobile devices. It allows a huge number of the mobile clients simultaneously access data at anytime and anywhere in the wireless environments. Applications using wireless data broadcast to disseminate information include accessing stock activities and traffic conditions. Using index technologies on the broadcast file, i.e., selective tuning, can reduce a lot of energy consumption of the mobile devices without significantly increasing client waiting time. Most of the research work for selective tuning assumes that each data item broadcast on the wireless channel is fairly evenly accessed by mobile clients. In real-life applications, more popular data may be frequently accessed by clients than less popular ones, i.e., skewed access patterns. In this dissertation, to support efficiently selective tuning with skewed access patterns in the single-channel wireless environments, we first propose a skewed distributed index, SDI, on the uniform data broadcast, on which each data item is broadcast once in a broadcast cycle. Second, we propose a skewed index, SI, on the nonuniform data broadcast, on which a few popular data items are broadcast more frequently in a broadcast cycle than the others. The first proposed algorithm, SDI, considers the access probabilities of data items and the replication of index nodes. The proposed algorithm traverses a balanced tree to determine whether an index node should be replicated by considering the access probability of its child node. In our performance analysis and simulation results, we have shown that our proposed algorithm outperforms the variant-fanout tree index and the distributed index. The second proposed algorithm, SI, applies Acharya et al.'s Broadcast Disks to generate a broadcast program, in which the popular data items are broadcast more times than the others, in order to reduce client waiting time. Moreover, the proposed algorithm builds a skewed tree for these data items and allocates index nodes for the popular data items more times than those for the less popular ones in a broadcast cycle. From our performance analysis and simulation results, we have shown that our proposed SI outperforms the flexible index and the flexible distributed index.