8 research outputs found
4G/LTE channel quality reference signal trace data set
Mobile networks, especially LTE networks, are used more and more for high-bandwidth services like multimedia or video streams. The quality of the data connection plays a major role in the perceived quality of a service. Videos may be presented in a low quality or experience a lot of stalling events, when the connection is too slow to buffer the next frames for playback. So far, no publicly available data s
Alcan Aluminium Limited v. Franchise Tax Board: State Unitary Apportionment of Foreign Parent Income Taxation Will Have to Go to State Court
Viewers using HTTP Adaptive Streaming (HAS)
without sufficient bandwidth undergo frequent quality switches
that hinder their watching experience. This situation, known
as instability, is produced when HAS players are unable to
accurately estimate the available bandwidth. Moreover, when
several players stream over a bottleneck link, their individual
adaptation techniques may result in an unfair share of the
channel. These are two detrimental issues in HAS technology,
which is otherwise very attractive. To overcome them, a group
of solutions are proposed in the literature that can be classified
as network-assisted HAS. Solving stability and fairness only in
the player is difficult, because a player has a limited view of
the network. Using information from network devices can help
players in making better adaptation decisions. In this paper we
describe our implementation in the form of an HTTP prox
Improving mobile video quality through predictive channel quality based buffering
Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources
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