Consistency-Aware Multipath Transport (CAMP) toward Interactive Multimodal LLM-Based Systems
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| Last updated | 2026-09-01 | ||
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draft-song-tsvwg-camp-01
TSVWG Y. Song
Internet-Draft Pengcheng Laboratory
Intended status: Informational C. Li
Expires: 6 March 2027 East China Normal University
Q. Li
Pengcheng Laboratory
2 September 2026
Consistency-Aware Multipath Transport (CAMP) toward Interactive
Multimodal LLM-Based Systems
draft-song-tsvwg-camp-01
Abstract
With the prosperity of generative large language models (LLMs),
interactive LLM-based services, such as digital humans, have imposed
new stringent requirements on low latency and high multimodal
consistency. Traditional interactive LLM-based systems typically
transmit multimodal content over a single network path, thereby
failing to exploit the advantages offered by multipath networks.
Even when multipath transport mechanisms are adopted, single-stream
encapsulation does not enable differentiated management of
heterogeneous modalities. However, naively separating modalities
into multiple streams further introduces inter-modal arrival
inconsistency. To address these challenges, this document specifies
CAMP, a consistency-aware multipath transport design over the
Multipath QUIC (MPQUIC) protocol. First, CAMP defines a three-stream
separation encapsulation format to support modality-differentiated
transmission. Second, it introduces a hierarchical multimodal data
management mechanism to coordinate the transmission of correlated
data across modality streams. Third, it incorporates a transport-
layer consistency-aware multipath scheduler to reduce inter-modal
arrival time deviation across network paths. Fourth, it specifies a
client-side application-layer alignment mechanism that operates in
coordination with the transport scheduler. To the best of our
knowledge, this is the first specification to address multipath-
enabled multimodal consistency guarantees for interactive LLM-based
systems.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Background and Challenges . . . . . . . . . . . . . . . . . . 4
2.1. Characteristics of LLM-generated Multimodal Data . . . . 4
2.2. Traditional Streaming Protocol . . . . . . . . . . . . . 4
2.3. Multipath Transport Basics . . . . . . . . . . . . . . . 5
2.4. Key Challenges . . . . . . . . . . . . . . . . . . . . . 5
3. Problem Statement and Design Principles . . . . . . . . . . . 6
3.1. Problem Statement . . . . . . . . . . . . . . . . . . . . 6
3.1.1. Multimodal Data Model . . . . . . . . . . . . . . . . 7
3.1.2. Multipath Network Model . . . . . . . . . . . . . . . 7
3.1.3. Scheduling Function . . . . . . . . . . . . . . . . . 7
3.1.4. Performance Metrics . . . . . . . . . . . . . . . . . 8
3.1.5. Overall Problem Definition . . . . . . . . . . . . . 8
3.2. Design Principles . . . . . . . . . . . . . . . . . . . . 8
4. Consistency-Aware Multipath Transport (CAMP) Design . . . . . 9
4.1. Overall Architecture . . . . . . . . . . . . . . . . . . 9
4.1.1. Server Side . . . . . . . . . . . . . . . . . . . . . 11
4.1.2. Multipath Transport Network . . . . . . . . . . . . . 11
4.1.3. Client Side . . . . . . . . . . . . . . . . . . . . . 12
4.2. Three-Stream Separation Format . . . . . . . . . . . . . 12
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4.3. Hierarchical Multimodal Data Management . . . . . . . . . 13
4.4. Consistency-Aware Multipath Scheduler . . . . . . . . . . 13
4.5. Client-Side Alignment Mechanism . . . . . . . . . . . . . 14
5. Security Considerations . . . . . . . . . . . . . . . . . . . 14
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1. Normative References . . . . . . . . . . . . . . . . . . 16
7.2. Informative References . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
Generative large language models (LLMs) have enabled a new generation
of interactive systems, including digital humans, conversational
agents, and multimodal assistants. These systems continuously
generate and transmit multimodal (i.e., text, audio, and video)
content in response to user inputs. Unlike traditional media
streaming or static content delivery, interactive LLM-based systems
operate under strict real-time constraints and require strong
semantic and temporal coupling across modalities.
Existing transport approaches for interactive LLM-based systems
predominantly rely on single-path transmission, which fails to
leverage the bandwidth aggregation, redundancy, and latency diversity
benefits offered by multipath networks. In addition, current
implementations typically encapsulate multiple modalities within a
single transport stream, which preventing fine-grained multimodal
data management. For example, text and audio generally require
reliable and in-order delivery, whereas video streams may tolerate
controlled frame loss to reduce latency.
A straightforward improvement is to separate text, audio, and video
into independent streams to enable differentiated transmission.
However, transmitting independent streams over heterogeneous paths
may introduce significant inter-modal arrival skew, leading to
multimodal inconsistency. Moreover, independently managing these
streams lacks coordination among correlated multimodal data.
Existing consistency mitigation mechanisms are primarily implemented
at the client-side application layer through post-arrival timestamp
alignment. However, they operate reactively and lack coordination
with the transport-layer multipath scheduler, leading to sub-optimal
performance. Furthermore, existing multipath transport mechanisms
primarily optimize for throughput or per-stream latency; they do not
consider multimodal consistency requirements intrinsic to interactive
LLM-based systems.
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Based on the above challenges, this document specifies Consistency-
Aware Multipath Transport (CAMP), a transport-layer design built on
Multipath QUIC (MPQUIC). CAMP introduces structured modality
separation, hierarchical multimodal data management, a consistency-
aware scheduler, and a coordinated client-side alignment mechanism.
The design does not modify the underlying MPQUIC protocol and is
deployable over existing MPQUIC implementations.
The remainder of this document is organized as follows. Section 2
provides background and summarizes the key challenges. Section 3
defines the problem and presents the design principles. Section 4
specifies the CAMP architecture and mechanisms. Section 5 discusses
security considerations, and Section 6 covers IANA considerations.
2. Background and Challenges
2.1. Characteristics of LLM-generated Multimodal Data
Interactive LLM-based systems generate heterogeneous yet semantically
coupled data streams, typically including text, audio, and video.
These modalities differ significantly in their traffic patterns,
reliability requirements, delay sensitivity, and tolerance to loss.
Text data is discrete, low-bandwidth, and generally requires reliable
and in-order delivery to preserve semantic correctness. Audio data
is continuous and delay-sensitive, requiring bounded jitter and
reliable delivery within strict playout deadlines. Video data is
bandwidth-intensive and latency-sensitive but can tolerate controlled
frame loss or quality adaptation in exchange for reduced delay.
Although these modalities exhibit distinct transport requirements,
they are derived from a unified generative process and therefore
require tight temporal alignment. For example, synthesized speech
must correspond to generated text, and video frames representing
facial animation must remain synchronized with spoken content.
Consequently, multimodal transmission for interactive LLM systems
requires both differentiated per-modality management and bounded
inter-modal arrival skew.
2.2. Traditional Streaming Protocol
Traditional real-time streaming protocols, such as the Real-Time
Messaging Protocol (RTMP), encapsulate audio and video data within
logical channels over a single transport connection, typically based
on TCP. These protocols were primarily designed for media
broadcasting and content distribution rather than interactive,
generative workloads.
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Such designs exhibit two fundamental limitations in the context of
interactive LLM-based systems. First, single-path transport cannot
leverage multipath bandwidth aggregation, redundancy, or latency
diversity. Second, multiple modalities are often multiplexed within
a single transport stream, preventing differentiated congestion
control, retransmission strategies, and scheduling policies. As a
result, heterogeneous modality requirements cannot be independently
satisfied.
These limitations make traditional streaming approaches unsuitable
for interactive LLM-based systems that demand both real-time
responsiveness and modality-aware management.
2.3. Multipath Transport Basics
Multipath transport protocols enable a single logical connection to
utilize multiple network paths concurrently. Multipath QUIC (MPQUIC,
as defined in [RFC9221]) extends QUIC (as defined in [RFC9000]) by
allowing multiple paths under a single connection context, each with
independent path characteristics. Multipath transport can provide
bandwidth aggregation, path redundancy, and potentially reduced
latency through dynamic path selection.
However, existing multipath scheduling algorithms primarily optimize
metrics such as throughput maximization, congestion window
utilization, or per-stream delivery latency. They do not explicitly
account for cross-stream temporal relationships. When independent
streams are distributed across heterogeneous paths with different
delay and jitter characteristics, arrival-time divergence across
streams may increase.
Therefore, while multipath transport improves network utilization and
resilience, it does not inherently guarantee multimodal consistency.
Moreover, existing multipath schedulers do not consider multimodal
inconsistency problem.
2.4. Key Challenges
Based on the above background, four key challenges are concluded in
transporting LLM-generated multimodal data:
1. *Inability of Single-Path Transmission to Exploit Multipath
Advantages*: Traditional single-path transport cannot leverage
multipath bandwidth aggregation, redundancy, or latency
diversity. This limitation constrains achievable performance in
real-time interactive environments.
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2. *Lack of Differentiated Multimodal Management under Single-Stream
Encapsulation*: Encapsulating text, audio, and video within a
single transport stream prevents modality-specific congestion
control, reliability policies, and scheduling strategies. As
interactive LLM systems impose heterogeneous requirements across
modalities, single-stream designs cannot satisfy differentiated
quality-of-service objectives.
3. *Lack of Cross-Stream Coordination under Independent Stream
Transmission*: Separating modalities into independent streams
enables differentiated transmission. However, independently
managing these streams lacks coordination among correlated
multimodal data. As a result, data from different modalities may
progress unevenly before multipath scheduling, introducing inter-
modal divergence even before the data are assigned to network
paths.
4. *Inter-Modal Arrival Inconsistency under Heterogeneous Multipath
Transmission*: When independent modality streams are scheduled
across heterogeneous paths, significant inter-modal arrival skew
may occur. This timing divergence degrades perceptual
synchronization and interactive quality.
5. *Insufficiency of Pure Application-Layer Alignment without
Transport-Layer Coordination*: Existing multimodal consistency
mechanisms are primarily implemented at the client-side
application layer through post-arrival timestamp alignment.
While such approaches can partially compensate for skew, they
operate reactively and lack coordination with transport-layer
multipath scheduling decisions. Consequently, latency and
consistency cannot be jointly optimized. Moreover, existing
transport-layer schedulers are not designed to be consistency-
aware and do not consider cross-modal timing constraints.
These challenges motivate the need for a consistency-aware multipath
transport design tailored to interactive multimodal LLM-based
systems.
3. Problem Statement and Design Principles
3.1. Problem Statement
In interactive LLM-based systems, multimodal outputs (i.e., text,
audio, video) are generated continuously and transmitted over
heterogeneous multipath networks. We aim to achieve low latency and
cross-modal consistency under path heterogeneity.
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3.1.1. Multimodal Data Model
Let M = {T, A, V} denote the set of modalities.
For each modality m in M, let S_m = {s_m,1, s_m,2, ...} denote the
ordered sequence of generated data units.
Each data unit s_m,k is associated with:
* generation time g_m,k
* size sigma_m,k
* deadline delta_m
* reliability indicator r_m, where r_m is either 0 or 1
For a given semantic synchronization index k, the set {s_T,k, s_A,k,
s_V,k} represents cross-modal correlated data units. In this
document, such a set of correlated data units is referred to as a
multimodal sample, and k serves as its sample identifier (i.e.,
sample ID).
3.1.2. Multipath Network Model
Let P = {p_1, p_2, ..., p_N} denote the set of available paths.
Each path p_i is characterized by:
* delay function D_i(t)
* available bandwidth b_i(t)
* loss probability l_i(t)
We assume heterogeneous paths, i.e., there exist i != j such that
D_i(t) is not equal to D_j(t).
3.1.3. Scheduling Function
Define a scheduling function \pi(.) such that \pi(s_{m,k}) = p_i
indicates that data unit s_{m,k} is transmitted over path p_i.
Let q_{m,k} denote the time at which s_{m,k} is admitted for
multipath transmission. The arrival time of s_{m,k} is then defined
as:
a_{m,k} = q_{m,k} + D_i(q_{m,k}),
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where p_i = \pi(s_{m,k}).
3.1.4. Performance Metrics
End-to-end latency L is defined as: L = average(a_m,k - g_m,k).
Inter-modal arrival skew for synchronization index k is defined as:
Delta_k = max_m(a_m,k) - min_m(a_m,k).
Expected cross-modal skew C is defined as: C = average(Delta_k). A
smaller C indicates better cross-modal consistency.
The resulting inter-modal arrival skew reflects the combined effects
of cross-stream admission divergence and heterogeneous multipath
transmission.
3.1.5. Overall Problem Definition
Given:
* modality set M
* multimodal data streams {S_m}
* heterogeneous path set P
* path dynamics {D_i(t), b_i(t), l_i(t)}
Find an admission policy and a scheduling function (\pi(.)) such
that:
* L is minimized
* C is minimized
* Reliability constraint holds: If r_m = 1, the loss probability of
s_m,k MUST be zero
* Deadline constraint holds: a_m,k - g_m,k <= delta_m
* Bandwidth constraint holds for each path p_i: The total
transmitted size assigned to p_i at time t MUST NOT exceed b_i(t)
3.2. Design Principles
Based on the above problem statement, the consistency-aware multipath
transport (CAMP) design for LLM-generated multimodal data is guided
by the following principles:
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1. *Modality Differentiation:* Different modalities (text, audio,
and video) exhibit heterogeneous requirements in reliability,
latency sensitivity, and rate characteristics. The transport
layer MUST support differentiated handling across modalities
rather than enforcing a single homogeneous policy.
2. *Cross-Stream Coordination*: While modalities are transmitted
through independent streams, correlated data across these streams
SHOULD be coordinated before multipath transmission. The
transport system SHOULD prevent one modality stream from
progressing excessively ahead of other modalities belonging to
the same multimodal sample.
3. *Consistency Awareness*: In interactive multimodal systems,
cross-modal arrival consistency is a first-order requirement.
Transport decisions MUST consider inter-modal arrival skew in
addition to per-flow latency. Minimizing latency alone is
insufficient.
4. *Coordination Between Transport and Application Layers*: Cross-
modal consistency is an end-to-end property that depends on both
transport-layer consistency control and receiver-side
application-level alignment mechanisms. Transport-layer data
management and multipath scheduling decisions influence arrival
ordering and skew, while the receiver performs timestamp-based
alignment and buffering. These mechanisms SHOULD be designed to
cooperate, rather than operate independently.
4. Consistency-Aware Multipath Transport (CAMP) Design
4.1. Overall Architecture
CAMP adopts an end-to-end architecture that integrates multimodal
generation, hierarchical multimodal data management, transport-layer
consistency-aware scheduling, and receiver-side alignment. The high-
level architecture is illustrated below.
+------------------------------------------------+
| Server |
|------------------------------------------------|
| LLM Application |
| (Multimodal Generation Engine) |
+-----------------------+------------------------+
|
v
+------------------------------------------------+
| Server Side: Application Layer |
|------------------------------------------------|
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| - Text Output |
| - Audio Output |
| - Video Output |
+-----------------------+------------------------+
|
v
+------------------------------------------------+
| Server Side: Transport Layer (CAMP) |
|------------------------------------------------|
| - Three Logical Streams |
| * Text Stream |
| * Audio Stream |
| * Video Stream |
| - Hierarchical Multimodal Data Management |
| - Consistency-Aware Multipath Scheduler |
| - Multipath Manager |
+-----------------------+------------------------+
|
==================================================
Heterogeneous Paths
(p1, p2, ..., pN with diverse settings)
==================================================
|
v
+------------------------------------------------+
| Client Side: Transport Layer (CAMP) |
|------------------------------------------------|
| - Stream Reassembly |
| - Path Reordering |
+-----------------------+------------------------+
|
v
+------------------------------------------------+
| Client Side: Application Layer |
|------------------------------------------------|
| - Timestamp-Based Alignment |
| - Cross-Modal Synchronization |
+-----------------------+------------------------+
|
v
+------------------------------------------------+
| Client |
| (Multimodal Rendering Engine) |
+------------------------------------------------+
The above architecture consists of two primary components: Server
Side and Client Side, with clear separation of responsibilities
between the transport layer and the application layer at both ends.
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The end-to-end design ensures that the transport layer and
application layer work in coordination to meet the low-latency and
consistency requirements of multimodal interactive systems.
4.1.1. Server Side
At the server side, the system is responsible for generating
multimodal data, including text, audio, and video, which are produced
by the LLM-based application. These modalities are generated by the
LLM Application (Multimodal Generation Engine), which processes user
inputs and produces multimodal outputs in real-time.
Once the data is generated, the Application Layer handles the
preparation of each modality for transmission. This layer separates
the modalities (text, audio, video) into different logical streams,
enabling modality-specific handling. For example, while text and
audio may require reliable transmission, video might be able to
tolerate some packet loss, and thus can be transmitted with a
different reliability strategy.
The Transport Layer (CAMP) then encapsulates each modality into its
own logical stream. Before multipath scheduling, CAMP applies
hierarchical multimodal data management to coordinate correlated data
across modality streams and reduce inter-modal divergence before path
assignment. The Consistency-Aware Multipath Scheduler then assigns
the managed data across multiple paths to further reduce inter-modal
arrival skew while optimizing the use of available bandwidth. It
takes into account the heterogeneity of the transport paths and the
varying needs of different data types (modalities).
4.1.2. Multipath Transport Network
The network is composed of multiple heterogeneous paths (denoted as
p1, p2, ..., pN) that offer diverse characteristics such as delay,
bandwidth, and packet loss. These paths are utilized by the CAMP
Transport Layer to maximize the available bandwidth and optimize
latency, while accounting for cross-modal consistency requirements.
CAMP uses multipath transport to enable the aggregation of bandwidth
and path redundancy. The consistency-aware multipath scheduling of
data across these paths aims to minimize the inter-modal arrival skew
caused by heterogeneous path characteristics. The transport layer
takes the different path characteristics into account to reduce
timing divergence among correlated data from different modalities
during multipath transmission.
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4.1.3. Client Side
On the client side, the data streams are received and reassembled by
the Transport Layer (CAMP). This layer is responsible for reordering
data packets that may have arrived out of order due to the diverse
paths used for transmission. The Multipath Manager handles data
received over different paths and provides the reassembled modality
streams to the application layer for subsequent alignment.
Once the data is reassembled, the Application Layer at the client
side performs timestamp-based alignment and cross-modal
synchronization to compensate for residual inter-modal skew. The
timestamp-based alignment mechanism uses the timing information
associated with the different modalities (text, audio, video) to
synchronize correlated multimodal data before they are presented to
the user. This synchronization is essential for interactive
multimodal applications like digital humans or virtual assistants,
where inconsistencies in the timing of different modalities would
disrupt the user experience.
The Client then renders the synchronized multimodal data, providing a
seamless experience for the user. The coordination between the
transport layer and application layer helps reduce latency while
maintaining consistency between modalities throughout the system.
4.2. Three-Stream Separation Format
The Three-Stream Separation Format is designed to handle multimodal
data (text, audio, and video) in a manner that ensures independent
management and optimized transmission for each modality. In this
format, each modality is encapsulated in its own logical stream
within the CAMP protocol. By separating these modalities, we can
apply different transmission policies tailored to the unique needs of
each type of data, such as reliability, latency, and throughput.
Each stream is identified by a unique stream identifier (e.g., T for
text, A for audio, V for video), allowing for differentiation and
independent handling. The encapsulation of each modality also
includes important metadata that is used to ensure the correct
delivery and synchronization of the streams. This metadata can
include generation timestamp, modality-specific parameters,
synchronization index, etc.
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By using separate streams and attaching the appropriate metadata, we
enable more efficient and precise transmission of each modality.
However, independent transmission of these streams may cause
correlated multimodal data to progress unevenly. CAMP therefore
coordinates data across the separated modality streams before
multipath scheduling, as described in the next section.
4.3. Hierarchical Multimodal Data Management
Although separate modality streams enable differentiated
transmission, independently managing these streams may cause
correlated multimodal data to progress unevenly before multipath
scheduling. To reduce such inter-modal divergence, CAMP introduces
Hierarchical Multimodal Data Management to coordinate data admission
across modality streams before assigning data to network paths.
At each send opportunity, CAMP considers the ready head-of-line data
from all modality streams and identifies the smallest sample ID f.
Only data whose sample ID does not exceed f+L are considered for
transmission, where L denotes the allowed lead window. This sample
frontier prevents one modality stream from progressing excessively
ahead of the others.
Within the sample frontier, CAMP prioritizes data from the earliest
ready sample and coordinates different modalities to promote sample
completeness. When multiple candidates are available, CAMP further
considers modality fairness, deadline, and remaining data size to
determine their transmission order. This hierarchical selection
prevents a single modality from persistently delaying the completion
of correlated multimodal samples.
To prevent large data units from monopolizing the transport queue,
CAMP limits the amount of data admitted from the selected stream at
each send opportunity to Q bytes. Together, the sample frontier,
hierarchical selection, and bounded admission reduce cross-modal
divergence before consistency-aware multipath scheduling.
4.4. Consistency-Aware Multipath Scheduler
Following hierarchical multimodal data management, the Consistency-
Aware Multipath Scheduler is responsible for optimizing the
transmission of multimodal data streams while ensuring that the
inter-modal timing remains consistent. By leveraging multipath
transport, the scheduler assigns each modality to the most
appropriate network path based on its specific needs. It considers
factors such as bandwidth, latency, and reliability, ensuring that
each stream (text, audio, or video) is transmitted under the
conditions that best suit its characteristics.
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The scheduler dynamically adjusts to changing network conditions,
such as congestion or fluctuating bandwidth, while also ensuring that
the timing between different streams remains consistent. It operates
by coordinating the scheduling of streams in a way that minimizes
timing skew between modalities, thus improving the overall
synchronization. This coordination with the transport layer enables
a consistent delivery of multimodal data, further reducing inter-
modal inconsistency during multipath transmission.
4.5. Client-Side Alignment Mechanism
he Client-Side Alignment Mechanism is responsible for ensuring that
multimodal data streams received by the client are synchronized
correctly before being rendered. Upon receiving the streams, the
client uses timestamps and synchronization indices, which were
embedded during the transmission process, to align the streams (text,
audio, and video) based on their respective generation times. This
ensures that the modalities are presented in a synchronized manner,
with minimal residual temporal divergence.
The alignment process involves using the application layer to adjust
the playback of each modality based on the synchronization index and
timestamps. In the event of minor discrepancies caused by network
conditions or transmission delays, the client employs techniques such
as buffering to mitigate these differences and ensure smooth
playback. This mechanism relies on continuous feedback from the
transport layer to adjust the synchronization strategy dynamically,
complementing the consistency-aware data management and multipath
scheduling performed at the transport layer.
5. Security Considerations
The CAMP protocol design includes several elements to ensure secure
transmission of multimodal data across different network paths.
However, as with any transport protocol, security considerations must
be addressed to prevent potential security threats.
1. *Data Integrity and Confidentiality*
Given that CAMP uses multipath transport to transmit multimodal
data across multiple paths, ensuring data integrity and
confidentiality is critical. We recommend leveraging the
existing encryption mechanisms of MPQUIC, which supports
encryption of data in transit using standard algorithms like
advanced encryption standard (AES). This ensures that data is
not compromised during transmission, preventing eavesdropping and
unauthorized access.
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2. *Authentication and Authorization*
As with any network protocol, ensuring that both sender and
receiver are authenticated is vital. CAMP relies on MPQUIC's
built-in authentication mechanisms, which leverage public key
infrastructure (PKI) or certificate-based authentication to
prevent unauthorized parties from participating in the
communication session.
3. *Replay and Man-in-the-Middle Attacks*
CAMP utilizes time-stamped data and synchronization indices to
coordinate multimodal streams. These timestamps should be
protected from tampering to prevent replay attacks or man-in-the-
middle (MITM) attacks. To mitigate this risk, CAMP ensures that
all communication channels are protected by end-to-end
encryption, and utilizes integrity protection schemes (e.g.,
HMAC) to verify the authenticity of the timestamps and
synchronization indices.
4. *DoS and DDoS Attacks*
The CAMP protocol should also consider protection against denial-
of-service (DoS) and distributed denial-of-service (DDoS)
attacks, where malicious users could flood the system with
excessive data, causing network congestion and delaying or
disrupting data transmission. To mitigate such attacks, CAMP
should implement rate limiting and congestion control mechanisms
as defined in the MPQUIC specification.
5. *Trust and Privacy Considerations*
Since CAMP is designed to handle multimodal data streams, it is
important to consider the privacy of the data being transmitted.
Any sensitive data, such as personally identifiable information
(PII), should be encrypted and anonymized when possible.
Additionally, careful access controls should be implemented to
ensure that only authorized entities can access the data.
These are just some of the key security considerations related to the
CAMP protocol. Future versions of the protocol may introduce
additional security measures as new threats emerge.
6. IANA Considerations
This document does not define any new protocol numbers, port numbers,
or other IANA-managed resources. However, future revisions of the
CAMP protocol may define new types, identifiers, or registry entries,
in which case the relevant considerations will be added.
Song, et al. Expires 6 March 2027 [Page 15]
Internet-Draft camp September 2026
If any new IANA registries or assignments are required in future
versions of this document, the authors will request the necessary
changes from IANA.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
7.2. Informative References
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/info/rfc9000>.
[RFC9221] Pauly, T., Kinnear, E., and D. Schinazi, "An Unreliable
Datagram Extension to QUIC", RFC 9221,
DOI 10.17487/RFC9221, March 2022,
<https://www.rfc-editor.org/info/rfc9221>.
Authors' Addresses
Yuhong Song
Pengcheng Laboratory
Email: songyh@pcl.ac.cn
Changlong Li
East China Normal University
Email: clli@cs.ecnu.edu.cn
Qing Li
Pengcheng Laboratory
Email: liq@pcl.ac.cn
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