RepCaM++: Efficient Neural Video Delivery

• The paper presents RepCaM++, a framework that integrates a shared content-aware super-resolution model with minimal chunk-specific prompts to drastically reduce transmission costs.
• It employs re-parameterization by collapsing enhanced multi-branch structures into a single compact inference model, maintaining high restoration quality with lower bandwidth load.
• The method achieves state-of-the-art performance on the VSD4K dataset, balancing detailed per-chunk adaptation with efficient client-side execution and reduced model payload.

RepCaM++ is a neural video delivery (NVD) framework for bandwidth-efficient Internet video streaming that combines a shared content-aware super-resolution (SR) model with ultra-light chunk-specific prompting. Its central objective is to retain the quality advantages of chunk-aware SR overfitting without incurring the prohibitive cost of transmitting a distinct adapted model for every video chunk. The framework consists of a Re-parameterization Content-aware Modulation (RepCaM) module, which expands training-time capacity and is algebraically collapsed back into the original backbone at inference, and a Transparent Visual Prompt (TVP), which supplies a minimal set of zero-initialized chunk-level parameters for residual local detail. On VSD4K, across six scenes and multiple video durations and scale factors, the method is reported to achieve state-of-the-art restoration quality together with lower delivery bandwidth among the strongest NVD baselines (Zhang et al., 17 Sep 2025).

1. Problem setting and design objective

RepCaM++ is formulated within the NVD paradigm in which the server transmits low-resolution (LR) video chunks and an SR model, and the client reconstructs high-resolution (HR) frames locally. If a video is split into $N$ chunks, each LR chunk has size $L$, and each chunk-specific SR model has size $S$, then classical chunk-wise NVD incurs transmission on the order of

$N \times (S + L).$

This scaling is the immediate systems bottleneck addressed by RepCaM++ (Zhang et al., 17 Sep 2025).

Chunk-specific adaptation exists because content-aware overfitting improves reconstruction fidelity: an SR network overfitted to a chunk reconstructs that chunk better than a generic pretrained model. The difficulty is that finer temporal partitioning improves adaptation while simultaneously increasing model-delivery cost. The paper identifies three linked limitations in earlier approaches such as NAS, CaFM, and EMT: parameter accumulation, delivery overhead, and scalability degradation on long videos. Prior methods either maintain chunk-specific parameter additions or distinct adapted weights, so transmission still grows with the number of chunks, and on sufficiently long videos the total model payload can approach or exceed the cost of sending the HR video itself. Methods that instead share one model across an entire video risk loss of chunk-specific fidelity when temporal homogeneity breaks down due to scene changes, foreground motion, and general temporal diversity (Zhang et al., 17 Sep 2025).

RepCaM++ is designed to break this tradeoff. The framework uses one shared inference-time model for the whole video, but enriches its training-time representational capacity so that multiple chunks can be absorbed into a unified modulation space. The remaining chunk-local variation is handled by an extremely small prompt mechanism. A plausible implication is that the method is best understood not as a generic SR approach but as a per-video overfitting system optimized for the communication-quality frontier of NVD.

2. End-to-end framework and transmission model

The server-side pipeline begins by dividing the original video into temporal chunks. A standard SR backbone is then selected; the paper lists EDSR, ESPCN, VDSR, and SRCNN as examples. The backbone is augmented with RepCaM modules, and a lightweight TVP is associated with each chunk and applied to input frames. The entire system is then trained content-aware on the video itself, following the NVD convention of training and testing on the same video frames. In the experiments, videos are often divided into 9 chunks, and the paper notes that a single TVP can be shared across multiple frames in a chunk to reduce redundancy (Zhang et al., 17 Sep 2025).

At deployment, the server sends the LR video chunks, one re-parameterized SR model shared across all chunks, and tiny TVP parameters. The resulting communication cost is approximately

$S + N \times (L + T),$

where $T$ is the TVP size. Because $T$ is extremely small, the paper often approximates this as

$S + N \times L.$

This contrasts directly with conventional chunk-wise model delivery: $$N(S+L) \quad \text{versus} \quad S + N(L+T) \approx S + NL.$$ The model payload is therefore paid once rather than once per chunk (Zhang et al., 17 Sep 2025).

Client-side inference is correspondingly compact. The client receives the re-parameterized single SR model, attaches the corresponding TVP to each LR chunk, and super-resolves the frames. The training-time multi-branch network is not executed on the client. This server-heavy, client-light asymmetry is an explicit deployment assumption of the method.

3. RepCaM module and inference-time re-parameterization

RepCaM is motivated by two observations cited in the paper: different branches with linear relationships produce different feature maps and gradient sensitivities for the same frame, and prior work, especially CaFM, observed that corresponding feature maps across chunk-specific SR models are approximately linearly related. RepCaM operationalizes this intuition as a parallel-cascade linear modulation structure inserted into the SR backbone (Zhang et al., 17 Sep 2025).

A standard convolution is replaced by a multi-branch structure in which branch $i$ applies $L$0 consecutive $L$1 convolutions followed by a $L$2 convolution. The RepCaM convolution is written as

$L$3

where $L$4, $L$5 denotes a $L$6 convolution, and $L$7 is a sequence of $L$8 consecutive $L$9 convolutions. Thus $S$0 is only a $S$1 convolution, $S$2 is $S$3, and so forth. The main implementation uses a 3-branch design. These training-time additions are the “parallel-cascade parameters”: parallel because multiple branches coexist, and cascade because each branch stacks $S$4 layers before the terminal $S$5 layer (Zhang et al., 17 Sep 2025).

The main architectural shift is that RepCaM is not chunk-specific in the sense of assigning a distinct weight set to each chunk. Instead, it enlarges the shared model during training so that one network can fit multiple chunk distributions more effectively. This is how parameter accumulation is avoided: all chunks are represented by a single shared model, and the extra training branches are removed before deployment.

The technical heart of the framework is structural re-parameterization. First, a cascade such as $S$6 is folded into a single equivalent $S$7 convolution. The paper expresses this as

$S$8

and states that there exists an equivalent single $S$9 convolution

$N \times (S + L).$0

The same procedure is applied recursively for branches with multiple $N \times (S + L).$1 layers (Zhang et al., 17 Sep 2025).

The second step merges the parallel branches into one inference-time convolutional layer. The paper describes this step using a concatenation-based formulation, even though the earlier training-time expression defines branch outputs as a sum. This discrepancy is one of the less formalized parts of the method. The conservative summary is that each branch is first folded into one equivalent $N \times (S + L).$2 convolution and that the branch parameters are then merged into a single inference-time convolution. The claimed result is unambiguous: the deployed model has the same parameter footprint as the original SR backbone, no extra inference-time parameter cost, and no model performance loss after folding (Zhang et al., 17 Sep 2025).

4. Transparent Visual Prompt

TVP is the component that upgrades RepCaM into RepCaM++. Its purpose is to restore chunk-specific fine detail that can still be blurred by a fully shared model on long or heterogeneous videos. The prompt is a learnable tensor

$N \times (S + L).$3

added additively to the centered region of the input frame or early input feature map. In the training details, the prompt size is $N \times (S + L).$4, and it is added to the center of the input video features. The center offsets are defined as

$N \times (S + L).$5

so the prompt occupies a spatially centered region and perturbs the input only within that region (Zhang et al., 17 Sep 2025).

The prompt is termed “transparent” because it is zero-initialized: $N \times (S + L).$6 At initialization, the prompted input is identical to the original input, so the prompt does not mask, replace, or occlude image content. This differs from conventional visual prompting, which is often randomly initialized to encourage generalization. Here, zero initialization is presented as beneficial precisely because the target regime is SR overfitting on a fixed video rather than generalization across videos. The paper argues that such initialization preserves image integrity from the start and stabilizes adaptation against prompt-induced corruption (Zhang et al., 17 Sep 2025).

TVP is described as capturing chunk-specific latent correction patterns related to foreground details, dynamic changes, and local texture cues. Its parameter cost is reported as below about 0.1%, and the abstract states “less than 0.1%.” The conclusion states less than 0.01%, but the main text consistently uses the rougher “below 0.1%” characterization. This suggests that TVP is intended as a negligible side-channel relative to both LR payload and model payload.

The paper also provides TVP update equations. It presents a reconstruction loss in simplified form and later states explicitly that the implementation uses L1 loss. The prompt gradient is defined through the additive injection, and the prompt is updated by gradient descent with learning rate $N \times (S + L).$7. The written loss expression is incomplete as a formal L1 or L2 objective, but the implementation description identifies L1 as the operative training loss (Zhang et al., 17 Sep 2025).

5. Optimization protocol and empirical results

The optimization setup uses Adam with $N \times (S + L).$8, $N \times (S + L).$9, $S + N \times (L + T),$0, learning rate $S + N \times (L + T),$1, and decay at 200 epochs. The training regime is content-aware: the model is trained and tested on the same video frames. Reported implementation details include scale factors $S + N \times (L + T),$2, $S + N \times (L + T),$3, and $S + N \times (L + T),$4; HR patch size $S + N \times (L + T),$5; video division into 9 chunks; prompt size typically $S + N \times (L + T),$6; sampling 1 frame out of 10 for testing; and training on an RTX 3090. To reduce training cost, the framework introduces Video Patch Sampling (VPS), which uses a simple classification sub-network to generate a weight map and then adaptively selects salient patches by a dot product with SR video frames (Zhang et al., 17 Sep 2025).

Evaluation is conducted on VSD4K, a dataset designed for video streaming and NVD evaluation. The six scenes are vlog, game, interview, city, sports, and dance, with durations of 15 s, 30 s, 45 s, 1 min, 2 min, and 5 min. HR videos are 1080p, and LR inputs are produced by bicubic downsampling. Metrics include PSNR, LPIPS, SSIM, consistency or smoothness via MSE between LR and downsampled SR, communication cost, FLOPS, GPU memory, and training time per iteration. Baselines include awDNN, NAS, CaFM, and EMT, together with SRVC, DVC, H.264, and H.265 in the corresponding comparison settings (Zhang et al., 17 Sep 2025).

The reported results show consistent gains across backbones, scales, and durations. With EDSR on 45 s videos, RepCaM++ improves over both CaFM and EMT on representative examples such as game-45s, where the PSNR triplets are $S + N \times (L + T),$7 for CaFM, $S + N \times (L + T),$8 for EMT, and $S + N \times (L + T),$9 for RepCaM++; inter-45s, where the corresponding values are $T$0, $T$1, and $T$2; and vlog-45s, where they are $T$3, $T$4, and $T$5 (Zhang et al., 17 Sep 2025).

Perceptual and structural metrics improve as well. On 45 s videos with EDSR, sport-45s LPIPS changes from $T$6 under EMT to $T$7 under RepCaM++, while dance-45s LPIPS changes from $T$8 to $T$9. For city-45s SSIM, the values change from $T$0 under EMT to $T$1 under RepCaM++ (Zhang et al., 17 Sep 2025).

The communication-quality summary on VSD4K-45s is particularly central to the method’s systems claim:

Method	Communication (MB)	PSNR
NAS	$T$2	32.87
CaFM	$T$3	33.12
EMT	$T$4	33.36
RepCaM++	$T$5	33.74

Consistency also improves from 3.542 under EMT to 3.320 under RepCaM++. On long videos, the method remains effective: for game-5min with EDSR, RepCaM++ reports $T$6 compared with $T$7 for RepCaM, $T$8 for EMT, and $T$9 for CaFM. Under equal storage budget, it also exceeds H.264 and H.265 in PSNR on the provided examples, such as game-45s at $S + N \times L.$0, where H.264 gives 39.26, H.265 gives 39.77, CaFM gives 43.32, RepCaM gives 43.46, and RepCaM++ gives 43.61 (Zhang et al., 17 Sep 2025).

6. Complexity, deployment profile, and limitations

RepCaM++ deliberately shifts cost from inference to training. During training, the model is larger than the original SR backbone because of multiple parallel branches, cascaded $S + N \times L.$1 convolutions, and TVP parameters. After re-parameterization, the extra branch parameters are absorbed and the deployed model matches the original backbone footprint, with only the tiny TVPs remaining as chunk-level side information. In the communication table, the inference FLOPS of RepCaM++ and EMT are both 9.18G, which is presented as evidence that inference complexity is effectively unchanged (Zhang et al., 17 Sep 2025).

The training-side overhead is substantial. Reported training time per iteration is 0.0046 sec/iter for RepCaM++, compared with 0.0014 for NAS, 0.0021 for CaFM, and 0.0027 for EMT. Training GPU memory is also much higher: 6421 MB for RepCaM++, versus 2341 MB, 2688 MB, and 3123 MB for NAS, CaFM, and EMT, respectively. These figures delimit the framework’s operating regime: abundant server-side compute and memory, compact client-side execution (Zhang et al., 17 Sep 2025).

Deployment claims extend to mobile execution. On a Huawei P50 Pro, the paper reports 29 FPS for EDSR-based RepCaM++ when upscaling from 270p to 1080p. The stated mobile real-time criteria are latency under 500 ms and over 20 FPS, and the reported mobile result satisfies those thresholds (Zhang et al., 17 Sep 2025).

Several caveats are explicit or directly inferable from the reported formulation. First, the approach remains a content-aware per-video overfitting pipeline, which may be operationally expensive at large streaming scale. Second, the re-parameterization description is directionally clear but mathematically incomplete in the branch-merging step. Third, the paper reports RepCaM versus RepCaM++ but does not provide a clean backbone-plus-TVP-only ablation. Fourth, the evaluation protocol reflects the NVD overfitting regime rather than cross-video generalization, because training and testing are performed on the same video. Finally, the current TVP is centrally placed and spatially fixed. The authors identify future work in quantization for content-aware models, pruning to reduce training cost, and discarding redundant frames with restoration via interpolation (Zhang et al., 17 Sep 2025).

RepCaM++ is therefore most precisely characterized as a server-trained, client-efficient NVD framework that replaces repeated transmission of chunk-specific SR models with one structurally over-parameterized but inference-compact shared model and negligible chunk-level prompts. Its significance lies in making chunk-aware content adaptation compatible with one-time model delivery, while preserving inference efficiency and improving the measured bandwidth-quality tradeoff on VSD4K (Zhang et al., 17 Sep 2025).