Robust Internet Video Transmission based on Scalable Coding Streams

When audio and video streams are transmitted over an unreliable IP network, it is not only the dependency relationship among packets but also the importance of information carried that should be taken into account in the selection of the best video-quality under variable network conditions. Therefore, many research efforts have been made towards improving performance of multimedia transmission systems in general, and designing partial reliability techniques for media content distribution over packet erasure channels which are usually considered on both the forward and feedback links. Efficient transmission over packet erasure channels can be realized by FEC, Automatic Retransmission reQuest (ARQ), error resilience, and error concealment [35]. FEC adds redundancy to the transmitted compressed bit-streams so that the receiver may correct errors and protect the video quality from degradation caused by packet loss and jitter. In ARQ, retransmission is requested when the receiver detects errors, and the most common schemes are: Stop-and-Wait ARQ, Go-Back-N ARQ, and Selective Repeat ARQ. In addition, a number of algorithms have been proposed in the literature for Hybrid ARQ (HARQ) which is essentially a combination of FEC with ARQ in an optimal strategy.

2.1.1 Two Basic Approaches to Error Correction

Automatic Retransmission reQuest (ARQ) is one of the commonly used channel coding approaches for error protection, which is particularly effective against burst channel errors. From a practical viewpoint, the main disadvantages of ARQ are the retransmission costs in terms of huge delays, and some receivers may receive duplicate packets which do not contribute to the quality of the stream due to the long delay propagation. Moreover, ARQ techniques require data stores for incoming packets and a feedback channel. The feedback channel is required to supply for the acknowledgment of error-free packet delivery or for packet retransmission request to the data sender.

FEC is an important low-delay mechanism which can be used as an open-loop

error control technique without the need of a feedback channel to reduce or eliminate packet losses in a video communication system. In a FEC scheme, the amount of FEC is pre-determined and based on the probability distribution of the packet loss. However, a larger amount of FEC or redundancy helps to recover from random packet loss event, but FEC codes are not designed to deal with packet burst loss. A burst error may cause substantial degradation to the transmitted video quality [36]. Hence, FEC with adaptive code rate to the importance of bits or frames would be more efficient for controlling errors in data transmission over unreliable channels.

2.1.2 Rate-Distortion Optimized Packet Scheduling

Some recent approaches [37-39] facilitate the selection of optimal channel-adaptive R-D strategies to minimize the expected loss-distortion for different multimedia applications with various quality-of-service requirements and system constraints. An R-D based framework is developed to describe discrete-time Markov chain models in characterizing the random packet-loss process associated with packet network transport system which is then used to analytically investigate the overall efficacy of packet-level protection in improving the end-to-end media quality. One of R-D optimized (RDO) streaming framework was proposed by Chou et al. [26]. RDO can be used to transmit a group of interdependent data unit before a delivery deadline by using feedback information and retransmission strategies. The RDO transmission policies can be obtained by minimizing the Lagrangian cost function of expected rate and distortion.

As shown in Fig. 2-8, the slope is the tangent to the convex hull of the R-D curve.

The RDO policy that minimizes the overall R-D Lagrangian cost: J(π)=D(π) + λR(π).

The parameter λ can control the operation of the basic conditions, such as distortion sensitive or rate sensitive. The expected distortion D(π) depends on the error probabilities, and the expected rate R(π) depends on the size of the data units.

D+λR

Figure 2-8: The set of R-D pairs, its lower convex hull, and an achievable pair (R, D)

... ^N

Figure 2-9: Trellis for a Markov decision process. Final state is indicated with double circles.

An example of RDO framework is illustrated in Fig. 2-9. In the figure, we assume that the deadline for the delivery (delivery deadline) prior to each discrete interval is the opportunity to send packets in each transmission opportunities S0, S1, ..., Sn-1. The sender will be based on the best strategies to select those data to be transmitted to the client. In the action phrase, the sender selects the data unit, then take action a0 = 1, or not to send data

unit, then take action a0 = 0. In the observation phrase, if the data unit receives feedback information o0 = 1, or if the unit has not received any information about the feedback message o0 = 0.In the RDO control streaming system, to decide which opportunities can be sent in each packet should be transmitted to the client is based on the deadline of this packet, the transmission histories, the channel statistics, feedback information, the interdependencies among packets, and the distortion reduction. The policy can indicate whether the video packet should be transmitted at each transmission opportunity. However, the scheme does not address the issue of reducing video quality variation over loss channels.

Furthermore, the RDO mechanism maps probability of packet losses into rate increment of redundant packet transmission. The drawback of this approach is that redundant packet transmission will make the resulting R-D curve impractical.

2.2 An Overview on Peer-to-peer Video Systems with