Life-transformative applications such as immersive

extended reality are revolutionizing wireless communications and

computer vision (CV). This paper presents a novel framework

for importance-aware adaptive data transmissions, designed

specifically for real-time CV applications where task-specific

fidelity is critical. A novel importance-weighted mean square

error (IMSE) metric is introduced as a task-oriented measure

of reconstruction quality, considering sub-pixel-level importance

(SP-I) and semantic segment-level importance (SS-I) models. To

minimize IMSE under total power constraints, data-importanceaware

waterfilling approaches are proposed to optimally allocate

transmission power according to data importance and channel

conditions, prioritizing sub-streams with high importance.

Simulation results demonstrate that the proposed approaches

significantly outperform margin-adaptive waterfilling and equal

power allocation strategies. The data partitioning that combines

both SP-I and SS-I models is shown to achieve the most

significant improvements, with normalized IMSE gains exceeding

7 dB and 10 dB over the baselines at high SNRs (> 10 dB).

These substantial gains highlight the potential of the proposed

framework to enhance data efficiency and robustness in real-time

CV applications, especially in bandwidth-limited and resourceconstrained

environments.