Both our experiments and previous studies show that LoRa links vary dynamically, which makes data transmission unreliable and consumes much energy of sensor nodes by retransmissions. This paper presents RALoRa, a Rateless-enabled link Adaptation system for LoRa networks. Rateless coding approaches the optimal data rate of a link by continuously transmitting encoded data with an initial data rate. However, LoRa’s modulation, Chirp Spread Spectrum (CSS), introduces unique challenges to rateless-enabled transmissions. With CSS, Spreading Factor (SF) simultaneously determines the initial data rate and the concurrent transmissions of multiple links. This dual role of SF requires the co-design of coding and networking. We thus formulate an optimization problem for allocating network resources (SF, frequency channels, and transmission power) and coding parameters (block size and packet size) to all sensor nodes. A key component of this formulation is a rateless-aware network model that estimates the data transmission results of sensor nodes based on their link quality and transmission setting. Given that the optimization problem for obtaining the best transmission setting of all sensor nodes is NP-complete, a two-stage heuristic algorithm is designed. A Kalman filter-based link quality predictor is developed to capture the link quality variation. We implement RALoRa on commodity LoRa hardware. Extensive experiments on a real testbed show that RALoRa extends the lifetime of LoRaWAN by 66.1%.