We propose a genetic algorithm -- MckpGen -- for rate scaling and
adaptive streaming of layered video streams from multiple sources
in a bandwidth-constrained environment. A genetic algorithm (GA)
consists of several components: a representation scheme; a
generator for creating an initial population; a crossover operator
for producing offspring solutions from parents; a mutation
operator to promote genetic diversity and a repair operator to
ensure feasibility of solutions produced. We formulated the
problem as a Multiple-Choice Knapsack Problem (MCKP), a variant of
Knapsack Problem (KP) and a decision problem in combinatorial
optimization. MCKP has many successful applications in fault
tolerance, capital budgeting, resource allocation for conserving
energy on mobile devices, etc. Genetic algorithms have been used
to solve NP-complete problems effectively, such as the KP,
however, to the best of our knowledge, there is no GA for MCKP. We
utilize a binary chromosome representation scheme for MCKP and
design and implement the components, utilizing problem-specific
knowledge for solving MCKP. In addition, for the repair operator,
we propose two schemes (RepairSimple and RepairBRP). Results show that RepairBRP yields significantly better performance. We further show that the average fitness of the entire population converges towards the best fitness (optimal) value and compare the performance at various bit-rates.
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