PMS

Author:

Bruneau-Queyreix Joachim1ORCID,Batalla Jordi Mongay2ORCID,Lacaud Mathias3,Negru Daniel4

Affiliation:

1. National Institute of Telecommunications, Warsaw, Joada, Bordeaux

2. National Institute of Telecommunications and Warsaw University of Technology

3. University of Bordeaux, Joada, Bordeaux

4. University of Bordeaux, Talence, France

Abstract

Single-source HTTP adaptive streaming solutions (HAS) have become the de facto solutions to deliver live video over the Internet. By avoiding video stalling events that are mainly caused by the lack of throughput at client or at server side, HAS solutions increase the end users’ quality of experience (QoE). We propose to pragmatically extend HAS with our MS-Stream solution that simultaneously utilizes several servers. MS-Stream aims at offering high QoE for live content delivery by exploiting expanded bandwidth and link diversity in distributed heterogeneous infrastructures. By leveraging end users’ connectivity capacities, we further extend the QoE and scalability capabilities of our proposal by exposing a hybrid P2P/multisource live-streaming solution (P2P/MS-Stream (PMS)), achieving trade-offs between the system’s scale and the end users’ QoE. We propose a distributed quality adaptation algorithm run by every peer, along with a local optimization method of the usage of the server infrastructure made available. Large-scale evaluations conducted with 300 peers located in France permits validating our approach and algorithms over flash crowd events and allow us to conclude that PMS can reach the optimal trade-offs between QoE and system scale.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. IoT Video Delivery Optimization Through Machine Learning-Based Frame Resolution Adjustment;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-05-24

2. Software‐defined content delivery network at the edge for adaptive video streaming;International Journal of Network Management;2022-08

3. DQ-DASH;ACM Transactions on Multimedia Computing, Communications, and Applications;2020-02-29

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