MS-Stream: A multiple-source adaptive streaming solution enhancing consumer's perceived quality

Joachim Bruneau-Queyreix (1, 2) , Mathias Lacaud (2) , Daniel Negru (1) , Jordi Mongay Batalla (3) , Eugen Borcoci (4)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 Viotech Communications
3 Warsaw University of Technology [Warsaw]
4 UPB - Université Polytechnique de Bucarest

Adaptive bitrate streaming protocols, such as DASH, have seen extensive interests for their adaptation capabilities to increase consumers' Quality of Experience (QoE) over the Inter-net, and have become de-facto standards in web video delivery. Compared with traditional single-server approaches, multiple-server streaming offers the opportunity to exploit expanded bandwidth, link diversity, and reliability. In this paper, we expose our solution for multiple-server support to dynamic adaptive streaming applications: Multiple-Source Streaming (MS-Stream). Thanks to its codec agnosticism and DASH-compliance our contribution is a pragmatic and evolving solution for QoE enhancement that can be applied to many streaming architec-tures (CDNs, Clouds) and is particularly suited for distributed environments such as P2P or Set-Top-Box overlays. In addition, splitting content into multiple independent sub-streams provides the opportunity to achieve easy-to-design bitrate adaptation and server-switching mechanisms. We empirically validate our approach using an extensive collection of network profiles provided by the DASH Industry Forum. Our solution is compared with the full potential of DASH with several servers over several QoE criteria. Results show the QoE gain of using MS-Stream against DASH; an online demonstration is made available.

Domaines: Réseaux et télécommunications [cs.NI]; Informatique [cs]

Joachim Bruneau-Queyreix, Mathias Lacaud, Daniel Negru, Jordi Mongay Batalla, Eugen Borcoci. MS-Stream: A multiple-source adaptive streaming solution enhancing consumer's perceived quality. IEEE Consumer Communications and Networking Conference - CCNC, 2017, Las vegas, United States. ⟨hal-01504005⟩