Streaming Abstention Mechanism

• Streaming Abstention Mechanism is a protocol-level framework enabling live video moderation by temporarily withholding GOP segments pending explicit analyzer approval.
• It extends MoQ transport with ANALYZE and FILTER parameters, allowing distributed, category-specific content analysis that governs segment delivery.
• The mechanism achieves minimal added latency—approximately one GOP duration—while ensuring compliance and uninterrupted playback for filtering subscribers.

A streaming abstention mechanism is a protocol-level framework designed to enable real-time content moderation in live, one-to-many video distribution over Media over QUIC (MoQ) transport. This mechanism introduces systematic, distributed abstention—temporarily withholding stream segments from delivery to end-users—pending explicit content-approval decisions by registered analyzers. The central goal is enforcement of safety, accessibility, and policy compliance in live streaming through segment-level eliminations, minimizing disruption to legitimate content flow and reducing moderation-induced latency to the minimum GOP (Group of Pictures) duration window (Freeman, 13 May 2025).

1. Extension of MoQ Transport: Messaging and Subscription Parameters

The mechanism leverages explicit protocol extensions to the emerging MoQ standard, introducing new parameters and control messages that distinguish between analyzers (clients or services performing content analysis) and filter consumers (clients requesting moderated streams). The approach modifies the SUBSCRIBE/SUBSCRIBE_UPDATE procedures to permit declaration of ANALYZE or FILTER capabilities for specific content categories:

Parameter Type	Role	Value
0x05	ANALYZE	Categories
0x06	FILTER	Categories

Wire format example:

ANALYZE or FILTER Parameter { Parameter Type (i) = 0x05 (ANALYZE) = 0x06 (FILTER) Parameter Length (i) Parameter Value = Categories { Categories Length (i) Number of Categories (i) [ Category Type (i), … ] } }

Approval of content for each GOP is communicated by analyzers via the APPROVE control message:

APPROVE Message { Type (i) = 0x41 Length (i) Subscribe ID (i) Group ID (i) Categories { Categories Length (i) Number of Categories (i) [ Category Type (i), … ] } }

The relay’s core logic is as follows: each incoming Group from the publisher is routed initially only to analyzer subscribers. Upon receipt of an APPROVE message for all relevant categories, the relay broadcasts approval. Filter-subscribing clients maintain per-session queues, and groups are released to playback strictly after all approvals. If any analyzer fails to approve—by veto or by failure—the group is dropped and does not reach filter subscribers.

2. Workflow: Stream Reception, Vetting, and Resumption

The abstention mechanism orchestrates real-time gating of video segments by segment category-checks at the relay. The canonical workflow for a filter-subscribing client is:

1. The publisher segments live video stream into Groups (GOPs).
2. The relay receives a Group and routes it to ANALYZE subscribers exclusively.
3. An analyzer inspects the Group. If no disallowed content is found, it issues an APPROVE; otherwise, it abstains (no APPROVE), effectively vetoing.
4. FILTER subscribers receive only those Groups that have received positive APPROVE for all their filtered categories.
5. Playback for a filter subscriber pauses if a Group is withheld and resumes as soon as the next eligible Group is approved.

This results in filtering manifesting as silent, segment-level skips, and rapid resumption upon compliance. The precise ordering can be illustrated as:

• Publisher → [Group N] → Relay → Analyzer A (inspects, optionally rejects)
• If approved: Relay broadcasts APPROVE, FILTER client receives [Group N]
• If withheld: FILTER client playback stalls, resumes at next approved Group

3. Latency Model and Mathematical Bound

The protocol’s central performance guarantee is the minimization of moderation-induced end-to-end latency to the GOP granularity. The maximum latency for a filtering subscriber $y$ filtering categories $C$ is given by:

$$L(y) = p + \max\{R(x)\} + \max\{F(x)\} + R(y) + \max(G)$$

where

• $p$: publisher-to-relay latency,
• $R(x)$: relay-to-analyzer $x$ latency,
• $F(x)$: analyzer $x$ to relay APPROVE latency,
• $R(y)$: relay-to-filter-subscriber $y$ latency,
• $G$: maximal GOP duration.

In local deployment (relay and analyzers co-located), $R(x) \approx F(x) \approx$ 1 ms, rendering the added latency for the filter subscriber nearly equivalent to a single GOP. This is achieved irrespective of the number or geographic distribution of analyzers, subject to high-performance relay implementation (Freeman, 13 May 2025).

4. Distributed Analysis, Fault Tolerance, and Adaptive Strategies

Subscription as ANALYZE is open to any client or server process, facilitating distributed, potentially crowdsourced moderation. Analyzer roles may be assigned or updated dynamically (the paper highlights future support for SUBSCRIBE_UPDATE-driven relay assignment). Adaptive load balancing, whereby clients analyze only under constrained resource utilization (e.g., CPU thresholds), is considered a prospective enhancement.

Fault tolerance is achieved by fail-open semantics: unapproved groups are silently dropped for filtering subscribers if an analyzer stalls or crashes. For each category, FILTER clients require unanimous approval from all registered analyzers, increasing robustness but yielding blocking if any analyzer is delayed. A practical deployment recommendation is to allocate multiple analyzers per critical category to mitigate single-point analyzer failure.

5. Experimental Evaluation and Measured Metrics

Empirical evaluation utilized a local testbed: a live webcam stream (1 s GOP) passed through an MoQ relay to two web clients. The analyzer client executed a strobe detection algorithm in WebAssembly, while the filter client performed playback. Metrics included:

• $\Delta$latency: Playback latency difference (filter minus analyzer)
• Detection correctness: Qualitative assessment for strobe detection
• Message propagation: Time for both Group and APPROVE messages

Observed results:

Metric	Value
GOP duration (G)	1 s
$\Delta$latency	994–1005 ms (≈ one GOP)
Group/APPROVE propagation	< 1 ms
Detection/Filter accuracy	No false positives/negatives
Buffer underruns (filter)	0 (once analyzer is ahead)
CPU/memory overhead	$\mathcal{O}$(categories)
Relay broadcast overhead	< 100 μs per Group

Filtering was found to be effective at segment exclusion without sacrificing playback stability, and the cost of approval tracking and broadcast scales linearly in the number of categories.

6. Limitations, Granularity, and Prospective Enhancements

The abstention mechanism operates at GOP granularity; this constrains eliminations to entire GOP segments even if only a subset (e.g., individual frames) is objectionable. Sub-GOP chunking and chunk-level analysis are cited as areas under investigation, potentially via MoQ WARP subsegmenting. The worst-case analysis latency is one full GOP, set by the necessity to receive the complete Group before analyzer processing can begin. Pipeline-based approaches could potentially reduce this bound.

Integration with adaptive bitrate streaming and multi-representation delivery is not addressed; ensuring synchronized approvals without redundant analysis across representations is identified as an open issue. The design contemplates, but does not yet standardize, relay-driven dynamic analyzer assignments for load adaptation. The security and authenticity of analyzer code and decisions is recognized as a requirement for deployment at scale, with hardware attestation and code provenance mechanisms as prospective research topics (Freeman, 13 May 2025).

7. Implications and Context

The streaming abstention mechanism addresses a central challenge in real-time content moderation: reconciling the legally or ethically mandated removal of objectionable content with the latency constraints of interactive live video. By moving analysis and abstention enforcement to a distributed client/server mesh, and by providing protocol-level support for abstention and resumption, the system minimizes user-perceived disruption and infrastructure overhead. This strategy suggests broader applicability to low-latency, multi-source moderated streaming, and may inform further protocol work in packetized transport security and real-time digital policy enforcement frameworks (Freeman, 13 May 2025).