Latency-Adjustable Encoder

• Latency-adjustable encoders are adaptive architectures that dynamically adjust encoding parameters to meet latency constraints while maintaining high task accuracy.
• Design strategies such as content-aware resolution adaptation, layer pruning, and early exiting enable efficient trade-offs in diverse domains like live streaming and ASR.
• Empirical results show significant gains, including up to 2.9× faster inference and reduced computational resource usage with minimal quality loss.

A latency-adjustable encoder is a class of neural or algorithmic encoder architectures incorporating mechanisms for explicit control or adaptation of encoding latency, often with the goal of achieving favorable trade-offs among latency, computational cost, and task accuracy. This concept is central in low-latency applications such as live video streaming, speech recognition, machine translation, natural language understanding, and edge-based vision systems, where encoding speed and responsiveness directly impact end-to-end system quality and user experience.

1. Formal Principles and Optimization Formulations

At the core of latency-adjustable encoder design is the formulation of encoding as a constrained optimization problem, where the encoder must satisfy a target latency or throughput constraint (e.g., $\text{encoding speed} \geq \text{video framerate}$ in live streaming) while maximizing coding efficiency (e.g., PSNR, VMAF, accuracy) and minimizing resource usage (e.g., CPU threads) (Menon et al., 2024).

A general formalism is as follows. For a set of representations (e.g., video bitrate-resolution pairs), select encoder parameters (preset, thread count, or resolution) to

• maximize coding quality $v_t=f_V(\cdot)$,
• guarantee latency $s_t = f_S(\cdot)\geq s_T$ or encoding time $T_{i,b}(r)\leq L_\mathrm{max}$,
• and minimize computational resources $\sum_t n_t\leq N_\mathrm{total}$.

Machine-learned models (e.g., random forests) are typically trained to predict quality and latency metrics from content features and configuration parameters, enabling real-time content- and platform-adaptive selection of encoder settings at inference time (Menon et al., 2024, Menon et al., 2024).

2. Design Strategies for Latency Adjustment

Latency-adjustable encoders realize their adaptability via hierarchical design strategies, varying by data modality and task:

• Thread and Preset Selection (Video Streaming): Joint optimization over thread count and encoder preset for each video chunk, guided by predicted speed and quality, as in JALE (Menon et al., 2024).
• Content-aware Resolution Adaptation: Directly adjusting output resolution per segment to fit within latency budgets (e.g., LADRE uses random forest models to select the highest-quality feasible resolution) (Menon et al., 2024).
• Encoder Depth or Layer Pruning (Speech/NLP): Dynamically reducing the number or complexity of encoder layers at inference—via learned per-layer importance (e.g., via layer dropout or token pruning (Kachuee et al., 2022), collaborative learning (Shi et al., 2021), spiral cyclic skipping (Tsunoo et al., 1 Oct 2025)), or by offline tuning with a speedup parameter.
• Block Processing with Early Exiting: For streaming audio, process small chunks with partial stacks or terminate computation early when confidence exceeds a threshold, as in Spiralformer (Tsunoo et al., 1 Oct 2025).
• Frame-skipping and Output Rate Reduction (Speech): Insert pooling/funnel layers to compress sequence length and thus reduce total computation, with stride and block placement as direct latency knobs (Prabhavalkar et al., 2024).

A common theme is the existence of tunable inference-time hyperparameters (e.g., thread count, elimination rate, skip cycle, speedup coefficient) which can be continuously or discretely adjusted to adapt latency to device, network, or user constraints.

3. Latency vs. Quality Trade-Offs and Empirical Frontiers

Latency-adjustable encoders provide explicit mechanisms to sweep the accuracy-latency-resource trade-off curve. Key empirical results representative across domains include:

Paper / System	Latency Knob	Quality Drop (Task)	Latency/Resource Gain
JALE (Menon et al., 2024)	Thread/preset per stream	+1.32 dB PSNR, +5.38 VMAF	–72.7% storage, –37.9% energy
LADRE (Menon et al., 2024)	Max encoding time per segment	+0.58 dB PSNR (VVenC)	–84.17% encoding energy
Latency-Adj. Transformer (Kachuee et al., 2022)	$\alpha_{SC}$ speedup coeff	<1% accuracy drop (GLUE, LLMs)	2.9$\times$ speedup, up to 2.9$\times$ faster TTFT
Dynamic Encoder Transducer (Shi et al., 2021)	Active layer count/swapping	6–8% WER gain at 10–15% RTF savings via dynamic switching
Spiralformer (Tsunoo et al., 1 Oct 2025)	Skip cycle $p$, exit threshold $\tau$	<0.3 pp WER	21.6% emission delay reduction (LibriSpeech)
Extreme Frame Rate Reduction (Prabhavalkar et al., 2024)	Funnel stride $R$	+0.3–0.5 pp WER (VS)	5–10$\times$ lower latency, $L_\mathrm{total} < 150$ ms

The practical range of quality drop (i.e., degradation in PSNR, VMAF, WER, mIoU, accuracy) is usually constrained to sub-1% for text/video models or a small number of points for speech and vision, at substantial gains in encoding speed, energy, and device utilization.

4. Architectural Implementations and Mechanistic Approaches

The implementation pattern is highly modality-dependent:

• Video encoders (JALE, LADRE): Apply joint thread/preset or resolution selection using trained random forest regressors. Input features include per-segment DCT energy, gradients, luma/chroma intensities.
• Transformers (NLP/ASR): Compute attention context contribution metrics (ACC) to rank tokens for per-layer pruning. Prune least-important tokens determined in fine-tuning; at inference vary a global coefficient to sweep the speed-accuracy curve with no additional training (Kachuee et al., 2022).
• Speech encoders (DET, Spiralformer, Funnel/Frame Reduction): Drop layers randomly or in structured cycles during training, exposing sub-models for later selection or switching (layer dropout, collaborative learning (Shi et al., 2021)). Spiralformer achieves circular layer skipping in block processing, reducing latency with early-exit on high-confidence prediction (Tsunoo et al., 1 Oct 2025). In "Extreme Encoder Output Frame Rate Reduction", funnel pooling is distributed through the conformer stack, with optimal stride placement determined via empirical ablation (Prabhavalkar et al., 2024).
• Edge vision (ASTC/JPEG XS): Encoders are pruned to fixed block sizes/precinct heights and optional coding features are disabled, producing constant-bitrate encodings whose complexity is matched to edge hardware (Žádník et al., 2022).
• Hardware GPU encoders: Discrete low-latency (LL) and ultra low-latency (ULL) modes are exposed as runtime flags (lookahead, B-frame count, async depth) with minimal RD cost. Quality presets do not increase pipeline latency on hardware, separating resource-latency axes (Arunruangsirilert et al., 24 Nov 2025).

5. Training and Inference Procedures

Latency-adjustable encoders typically utilize specialized training procedures to align the train-test conditionality gap:

• Partial data augmentation (Seq2Seq): Random segment cropping or partial-utterance truncation ensures networks are robust to incomplete context under latency constraints (Huang et al., 2020, Liu et al., 2020).
• Collaborative learning: Jointly trains sub-encoders of multiple depths (each with a separate loss); knowledge-distillation (KL divergence) encourages compact depth models to mimic full-depth representations (Shi et al., 2021).
• Pruning-driven fine-tuning: During encoder fine-tuning, infer per-layer token elimination ratios and globally optimize an offline speedup/latency coefficient applicable at runtime without retraining (Kachuee et al., 2022).
• Codec-specific retraining: In image compression for vision systems, networks are retrained on compressed-then-decompressed data to recover task accuracy lost due to aggressive codec pruning (Žádník et al., 2022).

Inference-stage adaptation is performed by tuning runtime parameters based on application constraints. Many systems permit continuous latency-resource tuning via a single exposed parameter, while others require discrete selection of a pruned subgraph, preset, or schedule.

6. Application Domains and Empirical Outcomes

Latency-adjustable encoders are foundational in:

• Adaptive live streaming: Real-time video bitrate ladder generation via JND-aware low-latency encoders or latency-aware dynamic resolution adaptation to optimize perceptual quality under user and compute constraints (Menon et al., 2024, Menon et al., 2024).
• ASR and streaming speech: Dynamic adjustment of encoder complexity for on-device or cloud ASR to meet user-perceived latency budgets (DET, Spiralformer, funnel encoders), or for end-to-end segment finalization in cascaded pipelines (Huang et al., 2022).
• NLP and LLM deployment: Transformers with runtime-pruned token counts for fast inference in language understanding, instruction tuning, or text generation (BERT/GPT/T5, Llama3, Mistral) (Kachuee et al., 2022).
• Edge vision: On-device classification or segmentation using fast codecs and post-retraining for robust DNN performance at sub-10 ms encoding latency (Žádník et al., 2022).
• Hardware video pipelines: Direct user-level control of trade-off between encoding delay and RD via hardware encoder settings (Arunruangsirilert et al., 24 Nov 2025).

Experimental results consistently indicate that latency-adjustable encoder architectures outperform fixed-latency baselines, offering significant improvements in energy efficiency, resource utilization, and/or end-user response time with minimal impact on final task performance.

7. Future Directions and Open Problems

Current research points to several active directions:

• Universal runtime interfaces: Unifying the low-level latency knobs (lookahead, layer skip, confidence, etc.) into abstracted APIs for platform-independent deployment (Arunruangsirilert et al., 24 Nov 2025).
• Online adaptation: Responsive encoder reconfiguration leveraging real-time feedback on device load, network jitter, or user interaction.
• Granular control: Finer-grained adjustability in block-structured or attention-based encoders, potentially with per-sample or per-token scheduling.
• Hybrid encoder architectures: Combining token/channel/column pruning, depth scaling, and conditional execution for deeper latency-accuracy trade-space coverage (Kachuee et al., 2022, Tsunoo et al., 1 Oct 2025).
• Extension to emerging modalities: Extending latency-adjustable encoding principles to new data types (e.g., point clouds, multi-modal, low-power IoT streams).

Limitations include the complexity of accurate latency/quality prediction for out-of-domain content, possible instability for very short sequences in token-pruned models, and trade-off curves that may have sharply non-linear knees beyond certain resource budgets (Prabhavalkar et al., 2024, Kachuee et al., 2022).

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In sum, latency-adjustable encoders constitute a mature, rigorously formalized paradigm for deploying real-time, resource-adaptive, high-quality encoding across domains, with well-quantified benefits substantiated by extensive empirical study (Menon et al., 2024, Menon et al., 2024, Kachuee et al., 2022, Shi et al., 2021, Arunruangsirilert et al., 24 Nov 2025).