1 research outputs found
Improving Crowdsourced Live Streaming with Aggregated Edge Networks
Recent years have witnessed a dramatic increase of user-generated video
services. In such user-generated video services, crowdsourced live streaming
(e.g., Periscope, Twitch) has significantly challenged today's edge network
infrastructure: today's edge networks (e.g., 4G, Wi-Fi) have limited uplink
capacity support, making high-bitrate live streaming over such links
fundamentally impossible. In this paper, we propose to let broadcasters (i.e.,
users who generate the video) upload crowdsourced video streams using
aggregated network resources from multiple edge networks. There are several
challenges in the proposal: First, how to design a framework that aggregates
bandwidth from multiple edge networks? Second, how to make this framework
transparent to today's crowdsourced live streaming services? Third, how to
maximize the streaming quality for the whole system? We design a
multi-objective and deployable bandwidth aggregation system BASS to address
these challenges: (1) We propose an aggregation framework transparent to
today's crowdsourced live streaming services, using an edge proxy box and
aggregation cloud paradigm; (2) We dynamically allocate geo-distributed cloud
aggregation servers to enable MPTCP (i.e., multi-path TCP), according to
location and network characteristics of both broadcasters and the original
streaming servers; (3) We maximize the overall performance gain for the whole
system, by matching streams with the best aggregation paths