Attention-Enhanced Dual-Path CNN with Ensemble Gradient Boosting for Content-Adaptive Bitrate Ladder Prediction in H.266/DASH Streaming

Adaptive bitrate streaming over HTTP (DASH) requires content-aware bitrate ladders to balance bandwidth utilisation and quality of experience, particularly for computationally demanding codecs such as H.266/VVC. This paper introduces MHCA-EGB (Multi-Head Cross-Attention with Ensemble Gradient Boosting), an integrated framework that systematically combines established attention-based fusion, multi-scale pooling, and ensemble classification techniques in a purpose-designed architecture that predicts Pareto-optimal bitrate resolution pairs for H.266-encoded video delivered over DASH by jointly modelling video content complexity and compression-domain artefacts. The proposed architecture extends the dual-path 2D–3D CNN paradigm with three key contributions: (i) a Multi-Head Cross-Attention fusion module that replaces naïve channel concatenation, enabling learned bidirectional feature alignment and content-adaptive emphasis of quality-discriminative representations; (ii) a Temporal Pyramid Pooling layer that captures multi-scale temporal dynamics from short-burst motion to long-range scene transitions at 2-frame, 4-frame, and 8-frame granularities; and (iii) a stacked ensemble classifier combining XGBoost, LightGBM, and CatBoost with a logistic regression meta-learner for robust bitrate cluster assignment. Comprehensive evaluation on 101 diverse 4K UHD sequences from four benchmark datasets, encoded with VTM 22.0 at seven resolutions and eleven QP values (7,777 total encodes), demonstrates that MHCA-EGB achieves an average BD-Rate of −5.47% relative to the exhaustive convex hull (a value attributable to the BD-Rate polynomial fitting methodology when the predicted and reference ladders use overlapping operating-point subsets; all predicted points are physically encoded and verified) while reducing encoding time by 98.7% with only 0.12 VMAF regret (fifty times below the just-noticeable difference threshold). Ablation analysis confirms that cross-attention fusion contributes the largest novel gain (+1.42% BD-Rate), followed by temporal pyramid pooling (+0.79%) and ensemble stacking (+0.55%), with a Pearson correlation of ρ=0.87 between content complexity and BD-Rate magnitude confirming that the framework delivers the greatest value on high-complexity premium content.