Exploiting Scalable Video Coding for Content Aware Downlink Video Delivery over LTE

We propose a content aware scheduler to allocate resources for video delivery on the downlink of a Long Term Evolution (LTE) network. We consider multiple users subscribe to a video streaming service, and request videos encoded in H.264 Scalable Video Coding format. The scheduler maximizes the average video quality across all users by assigning resource blocks based on their device capabilities, link qualities, and available resources. We measure video quality using two full reference metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index. We formulate the video delivery problem first as an integer linear program (ILP), and then reduce it to the multiple choice knapsack problem (MCKP). To solve the MCKP, we propose two fast heuristics with reduced processing overhead at the eNodeB, and a fully polynomial-time approximate scheme (FPTAS) using dynamic programming and profit-scaling. Our evaluation results indicate that the heuristics are within a factor of 1/2, and the FPTAS is very close to the optimal obtained from an ILP solver. We also propose a signaling mechanism to implement the content aware scheduler in existing LTE systems, and evaluate the impact of signaling delay on video distortion using both indoor and outdoor measurements collected from AT&T and T-Mobile networks.