Tag Archives: Video streaming

Reducing Packet Loss in Real-Time Wireless Multicast Video Streams with Forward Error Correction

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Average packet loss without and with FEC

Wireless multicast suffers from severe packet loss due to interference and
lack of link layer retransmission. In this work, we investigate wether the
most recent Forward Error Correction (FEC) draft is suitable for realtime wireless multicast live streaming, with emphasis on three main points:
packet reduction effectivity, and latency and overhead impact. We design
and perform an experiment in which we simulate wireless packet loss in
multicast streams with a Gilbert model pattern of ≈ 16% random packet
loss. We check all FEC configurations (L and D values) within several
constraints: maximum 500 milliseconds repair window (latency impact),
66.67% overhead, and a maximum L value of 20. For all these L and D
values we stream the tractor sample three times, to avoid possible outliers
in the data. We show that packet loss reduction in the most recent FEC
draft is effective, at most reducing from ≈ 16% down to ≈ 1.02%. We
also show that low latency streaming can be conducted, but it requires a
minimum of 160 milliseconds additional latency for our stream file. The
overhead for such low latency can be as high as 66.67%.

A testbed to compare Dynamic Adaptive Streaming over HTTP network configurations

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Streaming video over the internet has become vastly popular over the
past decade. In recent years there have been a shift towards using the
Hypertext Transfer Protocol (HTTP) for delivery of layered, segmented,
video content. These solutions go under the name HTTP Adaptive bit-rate
Streaming (HAS). One of the streaming solutions using this is the recent
international streaming standard Dynamic Adaptive Streaming over HTTP
(DASH). The increased popularity of HAS has significantly increased the
chance that multiple HAS clients will share the same network bottleneck.
Studies show that this can introduce unwanted network characteristics,
but as of today there are no good way of running realistic evaluations of
how different network configurations will impact HAS players sharing the
same bottleneck. To solve this, we have set up a testbed capable of running
automated streaming sessions, and have performed three experiments
using the DASH industry forum’s reference player, DASH.js, to present the
capabilities of the testbed.