Video streaming is a household term
now-a-days and it is widely gaining a lot of
popularity among mobile users. A wide variety of
mobile devices,such as smart phones and tablets,
are equipped with multiple wireless network
interfaces. A lot of videos are streamed over the
Internet according to users preferences but the
question is how to efficiently and cost-effectively
improve video streaming quality. In order to
maintain high video streaming quality while
reducing the wireless service cost,various
approaches such as improving band with using
adaptive algorithms are devised. In it,the optimal
video streaming process with WIFI is done using
bandwidth estimation and manipulation.Existing
systems consider the quality of service (QoS)
requirements for video traffic, such as the start up
latency, playback fluency, average playback quality,
playback smoothness and wireless service cost.
Existing systems based on different survey’s
include various bandwidth estimation tools such as
Spruce,Pathload,PathChirp etc.These bandwidth
estimation tools are based upon scenarios of probe
gap model and probe rate model.these tools help to
determine the available bandwidth based on the
transmission of packets between sender and receiver
the streaming quality is deduced and then the video
streaming quality is adjusted according to that
bandwidth as per user’s choice.
The rate adaptation decision is made at the client
side. For each segment, the client can request the
appropriate quality version based on its screen
resolution, current available bandwidth, and buffer
occupancy status. We can let the client request
different parts of one segment over different links.
The main contributions is based upon threefolds.
First, formulate the video streaming process over
multiple links as an MDP problem. To achieve
smooth and high quality video streaming, we define
several actions and reward functions for each state,
thus calculating the estimated bandwidth. Second,
to propose an algorithm to perform bandwidth
manipulation, this will take several future steps into
consideration to avoid playback interruption and
achieve better smoothness and quality. Last, we
implement a realistic test bed using an Android
phone and Scalable Video Coding (SVC) encoded
videos to evaluate the performance. [3]