488 research outputs found
Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks
Video streaming currently accounts for the majority of Internet traffic. One
factor that enables video streaming is HTTP Adaptive Streaming (HAS), that
allows the users to stream video using a bit rate that closely matches the
available bandwidth from the server to the client. MPEG Dynamic Adaptive
Streaming over HTTP (DASH) is a widely used standard, that allows the clients
to select the resolution to download based on their own estimations. The
algorithm for determining the next segment in a DASH stream is not partof the
standard, but it is an important factor in the resulting playback quality.
Nowadays vehicles are increasingly equipped with mobile communication devices,
and in-vehicle multimedia entertainment systems. In this paper, we evaluate the
performance of various DASH adaptation algorithms over a vehicular network. We
present detailed simulation results highlighting the advantages and
disadvantages of various adaptation algorithms in delivering video content to
vehicular users, and we show how the different adaptation algorithms perform in
terms of throughput, playback interruption time, and number of interruptions
Towards SVC-based adaptive streaming in information centric networks
HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE
EdgeDASH: Exploiting Network-Assisted Adaptive Video Streaming for Edge Caching
While edge video caching has great potential to decrease the core network
traffic as well as the users' experienced latency, it is often challenging to
exploit the caches in current client-driven video streaming solutions due to
two key reasons. First, even those clients interested in the same content might
request different quality levels as a video content is encoded into multiple
qualities to match a wide range of network conditions and device capabilities.
Second, the clients, who select the quality of the next chunk to request, are
unaware of the cached content at the network edge. Hence, it becomes imperative
to develop network-side solutions to exploit caching. This can also mitigate
some performance issues, in particular for the scenarios in which multiple
video clients compete for some bottleneck capacity. In this paper, we propose a
network-side control logic running at a WiFi AP to facilitate the use of cached
video content. In particular, an AP can assign a client station a different
video quality than its request, in case the alternative quality provides a
better utility. We formulate the quality assignment problem as an optimization
problem and develop several heuristics with polynomial complexity. Compared to
the baseline where the clients determine the quality adaptation, our proposals,
referred to as EdgeDASH, offer higher video quality, higher cache hits, and
lower stalling ratio which are essential for user's satisfaction. Our
simulations show that EdgeDASH facilitates significant cache hits and decreases
the buffer stalls only by changing the client's request by one quality level.
Moreover, from our analysis, we conclude that the network assistance provides
significant performance improvement, especially when the clients with identical
interests compete for a bottleneck link's capacity
Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends
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