Rate-distortion optimized client side rate control for adaptive media streaming

Abstract

Media streaming over unreliable networks such as the Internet is growing in popularity, but presents unique challenges when trying to get the user experience to be on par with classical mediums such as cable television. These networks have variable network conditions which not only vary between a set of points in the network, but also change over time. In this paper, we present a rate-distortion (R-D) optimized algorithm for adapting the bitrate of streaming media from a chunked encoding, where each chunk is available at multiple bitrates (or quality levels) or is scalable coded. The optimization takes into account the desired startup latency, the desired client buffer size, the current client buffer size, and the estimated network bandwidth. The problem is formulated as a distortion minimization problem subject to multiple rate constraints. For video content, the solution gives a gain of up to 3-4dB in PSNR in difficult portions of the video when compared to commonly used adaptation techniques and can achieve an arbitrarily small desired startup latency.

Virtual view adaptation for 3D multiview video streaming

“…Most of the proposed algorithms focus on smoothing the quality variations in a short interval in which the available bandwidth can be assumed constant (e.g., 2secs. in 19 ).…”

“…8 However, when used together with scalable video codecs, "multidimensional adaptation" is possible, where the video rate can be controlled by combining the scalability types supported by the codec . 9,15,16 Optimized adaptive streaming solutions can be also be designed with nonscalable codecs , 15,[17][18][19] at a higher storage cost and slightly higher transmission cost under certain bandwidth conditions. 15 Adaptive 3D multi-view streaming systems have received considerably less attention in the literature.…”

Layer Selection in Progressive Transmission of Motion-Compensated JPEG2000 Video

“…For example, in DASH [20], clients retrieve video streams, requesting (GOP by GOP) those code-stream segments that maximize the user's QoE (Quality of Experience), and the buffer fullness. In [21], Mehrotra et al propose an improvement of the previous approach in which clients use the R/D information of the video to select (taking into account the desired startup latency, the buffer size, and the estimated network capacity) the optimal number of quality layers (in the case of H.264/SVC), or which quality-version of each GOP (in the case of H.264/simulcasting) will be transmitted.…”

Streaming video over HTTP with consistent quality

“…We have identified several existing quality-based HAS schemes [13,9,10,7,8] (refer to Section 8 for discussions). However, they either focus on a different perspective (e.g., encoding, cross-stream optimization), or are based on different assumptions (e.g., scalable coded video source, statistically stationary source/channel models).…”

“…There have been several on-going efforts trying to tackle the video quality optimization problem for HAS, all from different perspectives. Mehrotra and Zhao consider an approach based on rate-distortion optimization and scalable video coding (SVC) [13]. They formulate the problem with the buffer constraint in a way similar to ours, and obtain a sub-optimal solution based on Lagrangian multiplier.…”