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COMETS: Coordinated Multi-Destination Video Transmission with In-Network Rate Adaptation

Yulong Zhang, Ying Cui, Zili Meng, Abhishek Kumar, Dirk Kutscher

TL;DR

COMETS tackles scalable, fair multi-destination video delivery by coupling information-centric networking with a distributed in-network optimization framework. It introduces a range-interested protocol and a dual-decomposition-based algorithm that performs per-hop, group-aware quality adaptation without centralized bottlenecks. The approach yields near-optimal QoE, strong fairness, and sub-50 ms convergence under hundreds of concurrent clients, outperforming DASH, MoQ, and ICN baselines. Practically, COMETS offers a deployable, scalable path for next-generation video delivery that leverages in-network aggregation and lightweight coordination to sustain quality at large scales.

Abstract

Large-scale video streaming events attract millions of simultaneous viewers, stressing existing delivery infrastructures. Client-driven adaptation reacts slowly to shared congestion, while server-based coordination introduces scalability bottlenecks and single points of failure. We present COMETS, a coordinated multi-destination video transmission framework that leverages information-centric networking principles such as request aggregation and in-network state awareness to enable scalable, fair, and adaptive rate control. COMETS introduces a novel range-interest protocol and distributed in-network decision process that aligns video quality across receiver groups while minimizing redundant transmissions. To achieve this, we develop a lightweight distributed optimization framework that guides per-hop quality adaptation without centralized control. Extensive emulation shows that COMETS consistently improves bandwidth utilization, fairness, and user-perceived quality of experience over DASH, MoQ, and ICN baselines, particularly under high concurrency. The results highlight COMETS as a practical, deployable approach for next-generation scalable video delivery.

COMETS: Coordinated Multi-Destination Video Transmission with In-Network Rate Adaptation

TL;DR

COMETS tackles scalable, fair multi-destination video delivery by coupling information-centric networking with a distributed in-network optimization framework. It introduces a range-interested protocol and a dual-decomposition-based algorithm that performs per-hop, group-aware quality adaptation without centralized bottlenecks. The approach yields near-optimal QoE, strong fairness, and sub-50 ms convergence under hundreds of concurrent clients, outperforming DASH, MoQ, and ICN baselines. Practically, COMETS offers a deployable, scalable path for next-generation video delivery that leverages in-network aggregation and lightweight coordination to sustain quality at large scales.

Abstract

Large-scale video streaming events attract millions of simultaneous viewers, stressing existing delivery infrastructures. Client-driven adaptation reacts slowly to shared congestion, while server-based coordination introduces scalability bottlenecks and single points of failure. We present COMETS, a coordinated multi-destination video transmission framework that leverages information-centric networking principles such as request aggregation and in-network state awareness to enable scalable, fair, and adaptive rate control. COMETS introduces a novel range-interest protocol and distributed in-network decision process that aligns video quality across receiver groups while minimizing redundant transmissions. To achieve this, we develop a lightweight distributed optimization framework that guides per-hop quality adaptation without centralized control. Extensive emulation shows that COMETS consistently improves bandwidth utilization, fairness, and user-perceived quality of experience over DASH, MoQ, and ICN baselines, particularly under high concurrency. The results highlight COMETS as a practical, deployable approach for next-generation scalable video delivery.
Paper Structure (28 sections, 2 theorems, 35 equations, 9 figures, 3 tables, 2 algorithms)

This paper contains 28 sections, 2 theorems, 35 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. COMETS System Design
  4. System Architecture
  5. Protocol Design
  6. Reliability, Congestion Control, and Security
  7. System Model
  8. Multi-user QoE Optimization
  9. Problem Formulation and Transformation
  10. Scalable Method
  11. Dual Decomposition Algorithm
  12. Experimental Evaluation
  13. Experimental Setup
  14. Evaluation Metrics
  15. Comparative Performance Evaluation
  16. ...and 13 more sections

Key Result

Lemma 1

The dual problem in dual_problem with the dual function given by dual_function is equivalent to the master dual problem in Problem master_prob together with the subproblems in Problems subprob_server, subprob_user, subprob_forwarder_x, and subprob_forwarder_y.

Figures (9)

  • Figure 1: Performance Comparison between baseline MoQ and server-optimized MoQ under increasing user load.
  • Figure 2: System Architecture. Forwarders optimize quality decisions based on client capabilities and available bandwidth.
  • Figure 3: Client-forwarder-server communication via interests.
  • Figure 4: Comparison of VMAF Scores
  • Figure 5: Comparison of Jitter Performance: (a) and (b) show two jitter metrics for an NDN-based system, while (c) compares jitter performance between MoQ and DASH-BOLA.
  • ...and 4 more figures

Theorems & Definitions (3)

  • Lemma 1: Decomposition of Dual Problem
  • Remark 1: Properties of Subproblem Solutions
  • Theorem 1: Convergence Analysis of Algorithm \ref{['alg:distributed_lagrangian']}