2,900 research outputs found
Dynamic Adaptive Point Cloud Streaming
High-quality point clouds have recently gained interest as an emerging form
of representing immersive 3D graphics. Unfortunately, these 3D media are bulky
and severely bandwidth intensive, which makes it difficult for streaming to
resource-limited and mobile devices. This has called researchers to propose
efficient and adaptive approaches for streaming of high-quality point clouds.
In this paper, we run a pilot study towards dynamic adaptive point cloud
streaming, and extend the concept of dynamic adaptive streaming over HTTP
(DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware
point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of
dense point cloud streaming while at the same time can semantically link to
human visual acuity to maintain high visual quality when needed. In order to
describe the various quality representations, we propose multiple thinning
approaches to spatially sub-sample point clouds in the 3D space, and design a
DASH Media Presentation Description manifest specific for point cloud
streaming. Our initial evaluations show that we can achieve significant
bandwidth and performance improvement on dense point cloud streaming with minor
negative quality impacts compared to the baseline scenario when no adaptations
is applied.Comment: 6 pages, 23rd ACM Packet Video (PV'18) Workshop, June 12--15, 2018,
Amsterdam, Netherland
The QUIC Fix for Optimal Video Streaming
Within a few years of its introduction, QUIC has gained traction: a
significant chunk of traffic is now delivered over QUIC. The networking
community is actively engaged in debating the fairness, performance, and
applicability of QUIC for various use cases, but these debates are centered
around a narrow, common theme: how does the new reliable transport built on top
of UDP fare in different scenarios? Support for unreliable delivery in QUIC
remains largely unexplored.
The option for delivering content unreliably, as in a best-effort model,
deserves the QUIC designers' and community's attention. We propose extending
QUIC to support unreliable streams and present a simple approach for
implementation. We discuss a simple use case of video streaming---an
application that dominates the overall Internet traffic---that can leverage the
unreliable streams and potentially bring immense benefits to network operators
and content providers. To this end, we present a prototype implementation that,
by using both the reliable and unreliable streams in QUIC, outperforms both TCP
and QUIC in our evaluations.Comment: Published to ACM CoNEXT Workshop on the Evolution, Performance, and
Interoperability of QUIC (EPIQ
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
HTTP based adaptive video streaming has become a popular choice of streaming
due to the reliable transmission and the flexibility offered to adapt to
varying network conditions. However, due to rate adaptation in adaptive
streaming, the quality of the videos at the client keeps varying with time
depending on the end-to-end network conditions. Further, varying network
conditions can lead to the video client running out of playback content
resulting in rebuffering events. These factors affect the user satisfaction and
cause degradation of the user quality of experience (QoE). It is important to
quantify the perceptual QoE of the streaming video users and monitor the same
in a continuous manner so that the QoE degradation can be minimized. However,
the continuous evaluation of QoE is challenging as it is determined by complex
dynamic interactions among the QoE influencing factors. Towards this end, we
present LSTM-QoE, a recurrent neural network based QoE prediction model using a
Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded
LSTM blocks to capture the nonlinearities and the complex temporal dependencies
involved in the time varying QoE. Based on an evaluation over several publicly
available continuous QoE databases, we demonstrate that the LSTM-QoE has the
capability to model the QoE dynamics effectively. We compare the proposed model
with the state-of-the-art QoE prediction models and show that it provides
superior performance across these databases. Further, we discuss the state
space perspective for the LSTM-QoE and show the efficacy of the state space
modeling approaches for QoE prediction
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