2 research outputs found
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