  Two main trends in today's internet are of major interest for video streaming
services: most content delivery platforms coincide towards using adaptive video
streaming over HTTP and new network architectures allowing caching at
intermediate points within the network. We investigate one of the most popular
streaming service in terms of rate adaptation and opportunistic caching. Our
experimental study shows that the streaming client's rate selection trajectory,
i.e., the set of selected segments of varied bit rates which constitute a
complete video, is not repetitive across separate downloads. Also, the
involvement of caching could lead to frequent alternation between cache and
server when serving back client's requests for video segments. These
observations warrant cautions for rate adaption algorithm design and trigger
our analysis to characterize the performance of in-network caching for HTTP
streaming. Our analytic results show: (i) a significant degradation of cache
hit rate for adaptive streaming, with a typical file popularity distribution in
nowadays internet; (ii) as a result of the (usually) higher throughput at the
client-cache connection compared to client-server one, cache-server
oscillations due to misjudgments of the rate adaptation algorithm occur.
Finally, we introduce DASH-INC, a framework for improved video streaming in
caching networks including transcoding and multiple throughput estimation.
