  Semantic communication has emerged as new paradigm shifts in 6G from the
conventional syntax-oriented communications. Recently, the wireless broadcast
technology has been introduced to support semantic communication system toward
higher communication efficiency. Nevertheless, existing broadcast semantic
communication systems target on general representation within one stage and
fail to balance the inference accuracy among users. In this paper, the
broadcast encoding process is decomposed into compression and fusion to
improves communication efficiency with adaptation to tasks and
channels.Particularly, we propose multiple task-channel-aware sub-encoders
(TCE) and a channel-aware feature fusion sub-encoder (CFE) towards compression
and fusion, respectively. In TCEs, multiple local-channel-aware attention
blocks are employed to extract and compress task-relevant information for each
user. In GFE, we introduce a global-channel-aware fine-tuning block to merge
these compressed task-relevant signals into a compact broadcast signal.
Notably, we retrieve the bottleneck in DeepBroadcast and leverage information
bottleneck theory to further optimize the parameter tuning of TCEs and CFE.We
substantiate our approach through experiments on a range of heterogeneous tasks
across various channels with additive white Gaussian noise (AWGN) channel,
Rayleigh fading channel, and Rician fading channel. Simulation results evidence
that the proposed DeepBroadcast outperforms the state-of-the-art methods.
