Abstract: With the rapid development of extended reality， the metaverse， and digital twin applications， human-computer interaction is evolving from conventional audiovisual perception toward immersive experiences that integrate multimodal perception and real-time interaction. As a critical bridge between the physical world and the metaverse， haptic interaction is characterized by multiple degrees of freedom， multiple contact points， and high sampling rates， which generate massive data volumes and exhibit stringent sensitivity to end-to-end latency at the millisecond level. Consequently， efficient haptic signal encoding and transmission have become major bottlenecks limiting interaction efficiency and user experience. However， existing haptic coding methods often suffer from perceptual jitter， prediction mismatch， or excessive encoding latency. To address these issues， this paper proposes a Haptic Dynamic Local Linear Prediction Coding （HDPC） method by jointly exploiting the statistical properties of haptic signals and human haptic perception mechanisms. HDPC first applies an adaptive amplitude scaling to low-magnitude signals to improve quantization efficiency. It then removes temporal redundancy through dynamic local linear prediction by leveraging the local linearity and global continuity of haptic signals in the time domain. Furthermore， a perception-driven adaptive quantization strategy is employed to reduce perceptual redundancy while preserving high-fidelity haptic perception. Finally， run-length coding and entropy coding are applied to further enhance compression performance. Experimental results on the IEEE P1918.1.1 standard haptic interaction dataset and a visuo-haptic integrated interaction platform demonstrated that， by flexibly adjusting the scaling factor， segment length， and perceptual quantization parameters， HDPC achieved significantly higher compression efficiency than existing mainstream haptic coding methods while maintaining very high signal fidelity. The proposed method effectively improves haptic signal transmission efficiency and interaction quality in immersive media systems under heterogeneous network conditions， providing valuable technical support for the efficient signal processing and system implementation of multimodal interaction in immersive media.