BALANCE: Limit-Aware Adaptive Video Enhancement

• BALANCE is a framework that fuses adaptive bitrate streaming with explicit operational limits and content-aware signals to optimize netcast quality.
• It employs techniques such as Pareto-front optimization, dynamic programming, and client-side enhancement to balance bitrate, quality, and resource constraints.
• Empirical evaluations demonstrate significant bitrate savings, reduced decoding time, and improved QoE across diverse adaptive streaming scenarios.

Bitrate-Adaptive Limit-Aware Netcast Content Enhancement (BALANCE) denotes a class of adaptive video delivery designs that couple bitrate selection with explicit operational limits—such as bandwidth, decoding time, energy, buffer occupancy, data caps, or cache/network constraints—and use content-aware signals to improve perceived quality under those limits. In the cited literature, BALANCE appears both as a unifying systems blueprint built from Pareto-front ladder optimization, client-side enhancement, and bandwidth-efficient buffer control, and as a specific data-cap-constrained QUBO formulation for segment-level bitrate allocation (Katsenou et al., 15 Jan 2026, Yang et al., 2022, Su et al., 2024, Rajpurohit et al., 23 Sep 2025).

1. Conceptual scope and research lineage

BALANCE sits at the intersection of adaptive bitrate streaming, content-adaptive encoding, and constrained optimization. Its defining property is not a single algorithmic core but the joint treatment of three interacting elements: a content model, a control variable that can be changed online or offline, and an explicit notion of limits. Those limits vary by formulation. In some instances they are device-side, such as decoding time and energy; in others they are network-side, such as throughput, latency, cache placement, or peer availability; in still others they are user- or service-level constraints such as a session data cap (Katsenou et al., 15 Jan 2026, Su et al., 2024, Rajpurohit et al., 23 Sep 2025).

The broader lineage of BALANCE spans several strands of adaptive streaming research. Finite-horizon dynamic programming for consistent quality in HTTP Adaptive Streaming established the importance of segment-level rate–distortion visibility and buffer-aware planning (Li et al., 2014). Backward-Shifted Coding introduced time-shifted base and enhancement layers to balance robustness, smoothness, and capacity utilization under HAS constraints (Ye et al., 2016). Contextual and Bayesian online decision methods formalized ABR as sequential decision-making under uncertainty (Alt et al., 2019). Content-aware and personalized adaptation introduced segment-level semantic weighting through Content-of-Interest scores (Gao et al., 2018). Cache-aware delivery work showed that bitrate adaptation and in-network storage interact nontrivially, and that bitrate placement itself can become a QoE control variable (Li et al., 2019).

Within this lineage, BALANCE functions as an architectural label for systems that make those previously separate components cohere. This suggests that BALANCE is best understood as a systems-level doctrine of adaptive streaming rather than as a single codec-specific or optimizer-specific method.

2. Optimization formulations

Across the BALANCE literature, several mathematical formulations recur. They differ in control variables and constraints, but they share a common structure: maximize a quality-related objective while constraining a resource budget or penalizing violations.

Formulation	Decision variables	Representative source
Pareto-front ladder design	$x=(r,q)$, with $f(x)=[R(x),T(x),-Q(x)]$	(Katsenou et al., 15 Jan 2026)
Joint bitrate/enhancement ABR	$a_i=(R_i,p_i)$	(Yang et al., 2022)
Wastage-aware buffer control	$(R_k,\Delta t_k)$ with BDV minimization	(Su et al., 2024)
Segment-level data-cap QUBO	$x_{i,j}\in\{0,1\}$ under $D_{\max}$	(Rajpurohit et al., 23 Sep 2025)

In the multi-objective VVC ladder formulation, each representation corresponds to a resolution–QP pair with measured bitrate $R(x)=b$, decoding time $T(x)=\tau_D$, and perceptual quality $Q(x)=v$. Ladder construction is posed as minimizing bitrate and decoding time while maximizing quality through the objective vector

$f(x) = [R(x), T(x), -Q(x)].$

Pareto dominance is defined by $f(x)=[R(x),T(x),-Q(x)]$0 if $f(x)=[R(x),T(x),-Q(x)]$1, $f(x)=[R(x),T(x),-Q(x)]$2, and $f(x)=[R(x),T(x),-Q(x)]$3, with at least one strict inequality. Two scalarizations are then introduced: JRQT-PF, with

$f(x)=[R(x),T(x),-Q(x)]$4

and JQT-PF, with

$f(x)=[R(x),T(x),-Q(x)]$5

which induce different trade-offs between bitrate, quality, and decoding time (Katsenou et al., 15 Jan 2026).

In enhancement-enabled BALANCE, the action space expands from bitrate alone to joint bitrate-and-processing decisions. ENAVS uses

$f(x)=[R(x),T(x),-Q(x)]$6

where $f(x)=[R(x),T(x),-Q(x)]$7 toggles client-side enhancement, and defines per-chunk QoE reward as

$f(x)=[R(x),T(x),-Q(x)]$8

The state includes download and playback buffer occupancy, timing slack, recent throughputs, recent enhancement times, previous bitrate, and an MPD-side quality map $f(x)=[R(x),T(x),-Q(x)]$9 (Yang et al., 2022).

In wastage-aware BALANCE, the key object is buffered data volume $a_i=(R_i,p_i)$0, which is formally equated to instantaneous traffic wastage at departure time: $a_i=(R_i,p_i)$1 The control problem then becomes the joint selection of chunk bitrate $a_i=(R_i,p_i)$2 and inter-chunk waiting time $a_i=(R_i,p_i)$3, optimizing

$a_i=(R_i,p_i)$4

or its receding-horizon counterpart over the next $a_i=(R_i,p_i)$5 chunks (Su et al., 2024).

In the quantum-annealing specialization, BALANCE becomes a discrete combinatorial allocation problem. Binary variables $a_i=(R_i,p_i)$6 indicate whether segment $a_i=(R_i,p_i)$7 uses quality level $a_i=(R_i,p_i)$8, and the primary objective is

$a_i=(R_i,p_i)$9

subject to one choice per segment and a global data cap. These constraints are embedded into QUBO either through slack variables or through a Dynamic Penalization Approach (DPA) (Rajpurohit et al., 23 Sep 2025).

3. Content-adaptive encoding and ladder construction

A central BALANCE task is to construct ladders that are simultaneously content-adaptive and operationally feasible. In the Pareto-front VVC framework, the offline pipeline is exhaustive and measurement-driven: downscale a native 2160p source to multiple target resolutions, encode each resolution–QP pair with VVenC, decode with VVdeC, upsample decoded outputs to 2160p, measure bitrate $(R_k,\Delta t_k)$0, decoding time $(R_k,\Delta t_k)$1, and quality $(R_k,\Delta t_k)$2, compute scalar scores, extract the Pareto front, sample representations at target bitrates, and enforce quality monotonicity across ladder rungs (Katsenou et al., 15 Jan 2026). Quality monotonicity is explicit: $(R_k,\Delta t_k)$3 whereas resolution monotonicity is not enforced. This is an important design choice: non-monotonic resolution switching is allowed as long as quality is non-decreasing (Katsenou et al., 15 Jan 2026).

Because exhaustive curve construction is expensive, several BALANCE-related works replace direct measurement with prediction. One model predicts both CRF–VMAF and CRF–bitrate curves at 101 discrete CRF points,

$(R_k,\Delta t_k)$4

using codec features, content features, and anchor features. Piecewise-linear interpolation yields $(R_k,\Delta t_k)$5 and $(R_k,\Delta t_k)$6, and their composition yields $(R_k,\Delta t_k)$7. An anchor suspension method shifts predicted curves by the difference between the anchor ground truth and the 1-pass prediction at $(R_k,\Delta t_k)$8, improving curve accuracy. The resulting system keeps the actual VMAF of the compressed video within 1 of the target with 99.14% accuracy (Yin et al., 2024).

A related transferable-ladder approach predicts VMAF from source features and a small set of fast-encode compression statistics, using Extra-Trees regression and then constructing a convex hull over predicted rate–quality points. Reported transfer losses relative to shot-wise convex hull are approximately 3.3% BD-Rate for libx265 presets, 6.7% for libsvtav1 presets, 5.2% for libvpx-vp9 presets, and 7.1% for libaom-av1 presets, with ensemble LLF2+VIFF+compression statistics achieving PLCC of about 0.965 at 2160p (Durbha et al., 15 Dec 2025).

Encoding-side BALANCE variants can also adapt the codec’s internal rate–distortion control. Per-clip and per-bitrate Lagrangian adaptation modifies the encoder multiplier through

$(R_k,\Delta t_k)$9

and then selects the best $x_{i,j}\in\{0,1\}$0 at each operating point, yielding “para-optimal gain” across bitrate and distortion (Ringis et al., 2022). GOP-level CARF, by contrast, predicts a GOP-specific CRF–bitrate model from x265 lookahead features: $x_{i,j}\in\{0,1\}$1 where $x_{i,j}\in\{0,1\}$2 are produced by a shallow neural network from features such as prediction encoding cost, AC score per MB, motion-vector length, and percentages of I MBs in P frames (Cheng et al., 2019). These methods are complementary to ladder construction: the former shape the points on the rate–quality curve, while the latter choose which points to expose.

4. Online limit-aware adaptation and client-side enhancement

BALANCE is not confined to offline encoding. Several formulations shift the control problem to the client, where bandwidth, compute, and latency constraints are only partially observable and change online.

The ENAVS-based blueprint augments a standard DASH/VoD pipeline with a download buffer, an enhancement module, and a playback buffer. Segments have fixed playback time $x_{i,j}\in\{0,1\}$3 s and bitrate ladder $x_{i,j}\in\{0,1\}$4 Mbps. Enhancement uses DNCNN, and the decision process is a Markov decision process solved by A3C with entropy regularization. Enhancement is explicitly deadline-constrained: if enhancement completion violates playback timing, the chunk incurs stall. The empirical comparison between enhancement and super-resolution is operational rather than purely perceptual: DNCNN runs at 98.9 FPS, CBDNet at 74.1 FPS, SRCNN $x_{i,j}\in\{0,1\}$5 at 47.4 FPS, and SRCNN $x_{i,j}\in\{0,1\}$6 at 51.9 FPS; at 2 Mbps, 1K after DNCNN reaches 37.20 dB, whereas 2 Mbps, 540p after SRCNN reaches 35.08 dB (Yang et al., 2022). The same work reports 5%–14% higher QoE than baselines under identical bandwidth and computing-power conditions (Yang et al., 2022).

Content-aware personalization introduces a different online signal: segment interestingness. In that setting, each chunk is assigned a Content-of-Interest weight, and the DQN reward becomes

$x_{i,j}\in\{0,1\}$7

where $x_{i,j}\in\{0,1\}$8 rescales interestingness and $x_{i,j}\in\{0,1\}$9 is the quality mapping. The system thereby reallocates bits toward semantically important segments without discarding the usual rebuffering and smoothness penalties (Gao et al., 2018).

Live-streaming BALANCE variants add latency and playout-speed control. HYSA combines latency-constrained bitrate selection, heuristic playback-rate control, and QoE-oriented adaptive frame dropping. It uses KAMA to predict segment bitrates and constrains playback speed to

$D_{\max}$0

with overall QoE reported as 2424.04 versus 2000.44 for MPC and 2038.34 for DTTB (Peng et al., 2019).

Condition-specialized RL provides another online pathway. ANT segments throughput traces into 20-second Network Throughput Segments, clusters them with $D_{\max}$1, and uses a multi-scale 1D CNN to infer the next condition. Each cluster selects a dedicated A3C ABR policy. On public traces, ANT achieves 1.575 average QoE per chunk, improving QoE by 65.5% over Pensieve and 31.28% over Oboe; on Tencent traces it reaches 1.79 average QoE per chunk, improving QoE by 25% over Pensieve and 12.44% over Oboe (Yin et al., 2021).

5. Data-cap, cache, and transport-aware BALANCE variants

A distinct branch of BALANCE makes resource limits first-class state variables rather than implicit penalties. The most explicit example is the data-cap formulation. The QUBO BALANCE framework pre-encodes each segment at multiple bitrate levels, measures $D_{\max}$2, and solves a one-choice-per-segment optimization under a global data limit $D_{\max}$3. The one-hot constraint is encoded by

$D_{\max}$4

and the cap is enforced either through slack variables or through the Dynamic Penalization Approach. DPA introduces an early penalty threshold

$D_{\max}$5

and the paper reports $D_{\max}$6, $D_{\max}$7, and $D_{\max}$8 as effective parameters. On D-Wave Advantage, with 1000 shots per run averaged over 10 trials, DPA yields higher probabilities of valid and optimal solutions than the slack-variable method (Rajpurohit et al., 23 Sep 2025).

Bandwidth-efficient BALANCE takes a different view of limits. Instead of a hard session cap, it treats over-buffering itself as latent wastage. The central variable is buffered data volume $D_{\max}$9, and the paper formalizes that if a user leaves at time $R(x)=b$0, wastage is exactly

$R(x)=b$1

To reduce $R(x)=b$2, BE-ABR jointly selects bitrate $R(x)=b$3 and inter-chunk waiting $R(x)=b$4, uses a Transformer-based time-aware delay predictor, and solves a QoE-constrained optimization. Reported results include a 60.87% wastage reduction with comparable, or even better, QoE than state-of-the-art methods (Su et al., 2024).

Cache-aware BALANCE extends limit awareness into the network core. RippleCache prioritizes high-bitrate segments near the edge and pushes low-bitrate fallbacks into the core, explicitly because ABR oscillations can be induced by heterogeneous cache hit distances. In the tractable setting, RippleClassic exceeds CE2-LFU by 9.4% average bitrate at $R(x)=b$5, while reducing oscillation and rebuffering relative to common baselines (Li et al., 2019). This reframes cache placement as a bitrate-control problem.

Transport-aware and decentralized variants operate even closer to the network substrate. eBandit relocates ABR-policy selection into the Linux kernel via eBPF, using live TCP metrics such as minimum RTT and instantaneous delivery rate. Its in-kernel epsilon-greedy bandit achieves $R(x)=b$6 cumulative QoE on an adversarial synthetic trace, 7.2% above the best static heuristic, and a mean QoE per chunk of 1.241 on 42 real-world sessions (Alizadeh, 9 Apr 2026). Stateless IPFS BALANCE removes synchronized provider-side state entirely, recomputing bitrate from local request-time signals and carrying short-lived adaptation context in HTTP headers; the reported design uses local signals $R(x)=b$7, $R(x)=b$8, $R(x)=b$9, cache status $T(x)=\tau_D$0, and buffer occupancy $T(x)=\tau_D$1, and early results indicate high QoE in faulty conditions, with improvements up to roughly $T(x)=\tau_D$2 over existing solutions (Mirzaei et al., 28 Jun 2026).

6. Empirical performance, assumptions, and misconceptions

Reported BALANCE results are heterogeneous because the underlying tasks differ: some papers optimize ladders offline, others optimize online bitrate selection, others optimize buffering, enhancement, caching, or data-capped segment plans. This suggests that direct metric-to-metric comparison is usually inappropriate; the more meaningful comparison is within each problem class.

Variant	Reported outcome	Source
JQT-PF, $T(x)=\tau_D$3	$T(x)=\tau_D$4 bitrate savings and $T(x)=\tau_D$5 decoding-time reduction at the same XPSNR vs. a fixed ladder	(Katsenou et al., 15 Jan 2026)
JRQT-PF, $T(x)=\tau_D$6	$T(x)=\tau_D$7 bitrate savings and $T(x)=\tau_D$8 decoding-time reduction	(Katsenou et al., 15 Jan 2026)
ENAVS	5%–14% higher QoE than baselines under the same bandwidth and computing power conditions	(Yang et al., 2022)
BE-ABR	60.87% wastage reduction with comparable, or even better, QoE	(Su et al., 2024)
QUBO BALANCE	DPA yields higher probabilities of valid and optimal solutions than the slack-variable method	(Rajpurohit et al., 23 Sep 2025)
Curve-prediction deployment	VMAF stays within 1 of target with 99.14% accuracy; online A/B gains of +0.107% video views, +0.107% video completions, and +0.064% app duration time	(Yin et al., 2024)

Several recurrent misconceptions are corrected by the literature. One is that quality-monotonic ladder construction requires resolution monotonicity. The Pareto-front VVC framework explicitly enforces quality monotonicity but does not enforce resolution monotonicity; non-monotonic resolution switching is allowed as long as quality does not decrease (Katsenou et al., 15 Jan 2026). Another is that bitrate savings and complexity savings are necessarily aligned. In the same framework, a more aggressive JQT-PF configuration yields up to $T(x)=\tau_D$9 bitrate savings at the cost of increased decoding complexity, whereas JRQT-PF offers more controlled trade-offs (Katsenou et al., 15 Jan 2026). A third is that enhancement and super-resolution are interchangeable. Under ENAVS’s conditions, enhancement is both faster and more PSNR-efficient than the tested super-resolution baselines, so compute feasibility becomes decisive (Yang et al., 2022).

A further misunderstanding is that BALANCE is intrinsically tied to quantum annealing. The presence of a QUBO formulation might suggest that, but the broader literature contains classical Pareto-front methods, dynamic programming, A3C, DQN, contextual bandits, kernel-resident multi-armed bandits, and Transformer-based control. This suggests that quantum annealing is one implementation route for one constrained allocation problem, not a prerequisite for BALANCE as such (Katsenou et al., 15 Jan 2026, Yang et al., 2022, Su et al., 2024, Rajpurohit et al., 23 Sep 2025).

The main assumptions and limitations are similarly recurrent. Decoding time is often treated as a proxy for energy, and strong pseudo-linear time–energy correlations are reported, but absolute values remain device-dependent (Katsenou et al., 15 Jan 2026). Offline per-title or per-shot optimization can be compute-intensive unless replaced by predictive models (Durbha et al., 15 Dec 2025, Yin et al., 2024). Some systems assume stable or calibratable relationships between bitrate, quality, and enhancement time, which may shift under new codecs, devices, or content classes (Yang et al., 2022, Ringis et al., 2022). Data-cap and wastage-aware variants depend on accurate delay or departure modeling; quantum variants additionally inherit minor-embedding and penalty-tuning difficulties (Su et al., 2024, Rajpurohit et al., 23 Sep 2025).

Taken together, the BALANCE literature defines a mature research program rather than a single mechanism. Its unifying idea is that bitrate adaptation becomes substantially more effective when it is made explicitly aware of the operative constraint set and when those constraints are fused with content-sensitive quality models rather than treated as exogenous afterthoughts.