ARXIV:2603.16823 · EDGE COMPUTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

Q: What products could be built from this research?

Now is the time because 5G deployment is expanding edge computing capabilities, and XR adoption is accelerating in gaming, education, and remote work. Market conditions favor solutions that reduce hardware costs by leveraging edge resources, and users increasingly expect all-day battery life from immersive devices.

Q: What are the practical use cases?

A cloud-based service that integrates with XR headsets and mobile devices to dynamically manage computation offloading to edge servers, used in enterprise training simulations where employees use AR glasses for hours without battery drain or lag.

Sourya Saha · Saptarshi Debroy · arXiv

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction…

METHOD

Full abstract

Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness while operating on energy- and battery-constrained devices, making execution placement between end devices and nearby edge servers a fundamental systems challenge. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime in closed-loop XR workloads. In this paper, we present a battery-aware execution management framework for edge-assisted XR systems that jointly considers execution placement, workload quality, latency requirements, and battery dynamics. We design an online decision mechanism based on a lightweight deep reinforcement learning policy that continuously adapts execution decisions under dynamic network conditions while maintaining high motion-to-photon latency compliance. Experimental results show that the proposed approach extends the projected device battery lifetime by up to 163% compared to latency-optimal local execution while maintaining over 90% motion-to-photon latency compliance under stable network conditions. Such compliance does not fall below 80% even under significantly limited network bandwidth availability, thereby demonstrating the effectiveness of explicitly managing latency-energy trade-offs in immersive XR systems.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experimental results show that the proposed approach extends the projected device battery lifetime by up to 163% compared to latency-optimal local execution while maintaining…

WHY NOW

Edge Computing moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.16823 · EDGE COMPUTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

Sourya Saha · Saptarshi Debroy · arXiv

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Edge Computing moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.16823

Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback28 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Edge Computing moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime in closed-loop XR workloads.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness while operating on energy- and battery-constrained devices, making execution placement between end devices and nearby edge servers a fundamental systems challenge. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime in closed-loop XR workloads.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experimental results show that the proposed approach extends the projected device battery lifetime by up to 163% compared to latency-optimal local execution while maintaining over 90% motion-to-photon latency compliance under stable network conditions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Edge Computing moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Edge Computing

Competitors

not indexed

Competitive landscape

A deep reinforcement learning framework for optimizing edge offloading in latency-sensitive XR applications.

Segment

Edge Computing

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.16823 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(28)

Understanding the Wi-Fi and VR streaming interplay: A comprehensible simulation and experimental study

2026Boris Bellalta, Miguel Casasnovas et al.

Resource Allocation for XR with Edge Offloading: A Reinforcement Learning Approach

2025Alperen Duru, Mohammad Mozaffari et al.

Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry

2025Sourya Saha, Md. Nurul Absur et al.

Towards Next Generation Immersive Applications in 5G Environments

2025Rohail Asim, Ankit Bhardwaj et al.

RemoteVIO: Offloading Head Tracking in an End-to-End XR System

2025Qinjun Jiang, Yihan Pang et al.

XRgo: Design and Evaluation of Rendering Offload for Low-Power Extended Reality Devices

2025Steven Gao, Jeffrey Liu et al.

Infer-EDGE: Dynamic DNN Inference Optimization in 'Just-in-time' Edge-AI Implementations

2025Motahare Mounesan, Xiaojie Zhang et al.

EdgeVerse: Multi-User Virtual Reality via Edge Computing and eBPF

2024Okwudilichukwu Okafor, Flavio Esposito et al.

Task offloading strategies for mobile edge computing: A survey

2024Shi Dong, Jun Tang et al.

Dependent Task Offloading and Resource Allocation via Deep Reinforcement Learning for Extended Reality in Mobile Edge Networks

2024Xiaofan Yu, Siyuan Zhou et al.

Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite

2024Xinzhe Liu, Yuanyuan Yang et al.

Edge Rendering Architecture for multiuser XR Experiences and E2E Performance Assessment

2024Inhar Yeregui, Daniel Mejías et al.

Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud

2024Motahare Mounesan, Mauro Lemus et al.

Experimental Evaluation of Interactive Edge/Cloud Virtual Reality Gaming over Wi-Fi using Unity Render Streaming

2024Miguel Casasnovas, C. Michaelides et al.

On Balancing Latency and Quality of Edge-Native Multi-View 3D Reconstruction

2023Xiaojie Zhang, Houchao Gan et al.

EFFECT-DNN: Energy-efficient Edge Framework for Real-time DNN Inference

2023Xiaojie Zhang, Motahare Mounesan et al.

Resource Management in Mobile Edge Computing: A Comprehensive Survey

2023Xiaojie Zhang, S. Debroy

Minimizing the Motion-to-Photon-delay (MPD) in Virtual Reality Systems

2023Akanksha Dixit, S. Sarangi

Will Edge Computing Enable Location based Extended/Mixed Reality Mobile Gaming? Demystifying Trade-off of Execution Time vs. Energy Consumption

2023A. Ometov, J. Nurmi

Power-efficient live virtual reality streaming using edge offloading

2022Zichen Zhu, Xianglong Feng et al.

Showing 20 of 28 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisDRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

6.0

Builds On ThisQAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks

3.0

Builds On ThisReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization

4.0

Builds On ThisCovariance-Guided Resource Adaptive Learning for Efficient Edge Inference

3.0

Builds On ThisCALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning

4.0

Builds On ThisTransferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

3.0

Builds On ThisTrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge

6.0

Builds On ThisDesign Rules for Extreme-Edge Scientific Computing on AI Engines

6.0

Builds On ThisCross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning

0.0

Builds On ThisCR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference

4.0

Commercially relevant

none indexed

Conflicting

none indexed

Related Resources

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.16823
Route: /paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines
Active tab: read
Artifact: deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines/paper-pack
REST build passport API/api/v1/paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

Canonical ID deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines | Route /paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

MCP example

{
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}

source_context

{
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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

/buildability/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

Watchwatch

Subject: Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

Paper ref

deep-reinforcement-learning-driven-edge-offloading-for-latency-constrained-xr-pipelines

arXiv id

2603.16823

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

fe7d8d4d74e8e1366203ea9f754f9fe2ea9489b8d7f650477c111c0eb93e2904

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

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