Low Latency Low Loss Media Delivery Utilizing In-Network Packet Wash

Author:

Clayman StuartORCID,Sayıt MügeORCID

Abstract

AbstractThis paper presents new techniques and mechanisms for carrying streams of layered video using Scalable Video Coding (SVC) from servers to clients, utilizing the Packet Wash mechanism which is part of the Big Packet Protocol (BPP). BPP was designed to handle the transfer of packets for high-bandwidth, low-latency applications, aiming to overcome a number of issues current networks have with high precision services. One of the most important advantages of BPP is that it allows the dynamic adaption of packets during transmission. BPP uses Packet Wash to reduce the payload, and the size of a packet by eliminating specific chunks. For video, this means cutting out specific segments of the transferred video, rather than dropping packets, as happens with UDP based transmission, or retrying the transmission of packets, as happens with TCP. The chunk elimination approach is well matched with SVC video, and these techniques and mechanisms are utilized and presented. An evaluation of the performance is provided, plus a comparison of using UDP or TCP, which are the other common approaches for carrying media over IP. Our main contributions are the mapping of SVC video into BPP packets to provide low latency, low loss delivery, which provides better QoE performance than either UDP or TCP, when using those techniques and mechanisms. This approach has proved to be an effective way to enhance the performance of video streaming applications, by obtaining continuous delivery, while maintaining guaranteed quality at the receiver. In this work we have successfully used an H264 SVC encoded video for layered video transmission utilizing BPP, and can demonstrate video delivery with low latency and low loss in limited bandwidth environments.

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Reference35 articles.

1. Belshe, M., Peon, R., Thomson, M.: Hypertext Transfer Protocol Version 2 (HTTP/2). RFC 7540

2. Bentaleb, A., Taani, B., Begen, A.C., Timmerer, C., Zimmermann, R.: A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surveys Tutorials 21(1), 562–585 (2019)

3. Bishop, M.: Hypertext Transfer Protocol Version 3 (HTTP/3). Internet-Draft draft-ietf-quic-http-34, Internet Engineering Task Force (February 2021)

4. Clark, D.D., Tennenhouse, D.L.: Architectural Considerations for a New Generation of Protocols. In: SIGCOMM, Philadelphia, ACM (1990) 200–208

5. Clayman, S.: The Inter-Dependence of Network Transport and Application Behaviour. In: ITU Network 2030, Geneva (October 2019)

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