  Video streaming today accounts for up to 55\% of mobile traffic. In this
paper, we explore streaming videos encoded using Scalable Video Coding scheme
(SVC) over highly variable bandwidth conditions such as cellular networks.
SVC's unique encoding scheme allows the quality of a video chunk to change
incrementally, making it more flexible and adaptive to challenging network
conditions compared to other encoding schemes. Our contribution is threefold.
First, we formulate the quality decisions of video chunks constrained by the
available bandwidth, the playback buffer, and the chunk deadlines as an
optimization problem. The objective is to optimize a novel QoE metric that
models a combination of the three objectives of minimizing the stall/skip
duration of the video, maximizing the playback quality of every chunk, and
minimizing the number of quality switches. Second, we develop Layered Bin
Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed
optimization problem. Moreover, we show that LBP achieves the optimal solution
of the proposed optimization problem with linear complexity in the number of
video chunks. Third, we propose an online algorithm (online LBP) where several
challenges are addressed including handling bandwidth prediction errors, and
short prediction duration. Extensive simulations with real bandwidth traces of
public datasets reveal the robustness of our scheme and demonstrate its
significant performance improvement as compared to the state-of-the-art SVC
streaming algorithms. The proposed algorithm is also implemented on a TCP/IP
emulation test bed with real LTE bandwidth traces, and the emulation confirms
the simulation results and validates that the algorithm can be implemented and
deployed on today's mobile devices.
