Multicast for Multirate Wireless LANs

Most research efforts on multicasting in IEEE 802.11
WLANs have focused on improving the service reliability by
integrating ARQ mechanisms into the protocol architecture.
The Leader-Based Protocol (LBP) ARQ mechanism has
been introduced to provide the multicast service with some
level of reliability. To address the ACK implosion problem,
LBP assigns the role of group leader to the multicast receiver
exhibiting the worst signal quality in the group. The group
leader holds the responsibility to acknowledge the multicast
packets on behalf of all the multicast group members, whereas
other MTs may issue Negative Acknowledgement (NACK)
frames when they detect errors in the transmission process.

The 802.11MX reliable multicast scheme described in
uses an ARQ mechanism supplemented by a busy tone signal.
When an MT associated to a multicast group receives a
corrupted packet, it sends a NACK tone instead of actually
transmitting a NACK frame. Upon detecting the NACK tone,
the sender will retransmit the data packet. Since the 802.11MX
mechanism does not need a leader to operate, it performs
better than the LBP protocol in terms of both data throughput
and reliability. However, this mechanism is very costly since
it requires a signaling channel to send the NACK frames and
busy tones. Moreover, both LBP and 802.11MX schemes do
not adapt the multicast PHY rate to the state of receivers.

Very recently, the RAM scheme has been proposed in
for reliable multicast delivery. Similar to the LBP and
802.11MX schemes, the transmitter has first to send a RTS
frame to indicate the beginning of a multicast transmission.
However, in RAM the RTS frame is used by all the multicast
receivers to measure the Receiver Signal Strength (RSS).
Then, each multicast receiver has to send a variable length
dummy CTS frame whose length depends on the selected
PHY transmission mode. Finally, the transmitter senses the
channel to measure the collision duration and can adapt the
PHY rate transmission of the multicast data frame accordingly.
This smart solution is more practical than 802.11 MX since
it does not require a signaling channel but still requires the
use of RTS/CTS mechanism and targets reliable transmission
applications.

In SNR-based Auto Rate for Multicast (SARM) is
proposed for multimedia streaming applications. In SARM,
multicast receivers measure the SNR of periodically broadcast
beacon frames and transmit back this information to the AP.
To minimize feedback collision, the backoff time to send
this feedback increases linearly with the received SNR value.
Then, the AP selects the lowest received SNR to adapt the
PHY rate transmission. The main problem with this approach
is that the transmission mode cannot be adapted for each
multicast frame. The multicast PHY rate of SARM is adapted
at each beacon intervals. SARM does not make use of any
error recovery mechanism, such as, data retransmission.

Note that at the exception of RAM and SARM, the mechanisms
just described above only focus on solving the reliability
of the multicast service in WLANs. Only RAM and SARM
adapt the PHY transmission rate of the multicast data frames.
In this paper, we define an architecture by integrating the
following facilities: 1) the optimal channel rate adaptation
of the multicast service in IEEE 802.11 WLANs, 2) a more
reliable transmission of the multicast data, 3) the limitation
on the overhead required by the signaling mechanism, and
4) the support of heterogeneity of receivers by using different
multicast groups and hierarchical video coding. The definition
of the proposed cross layer architecture is based on the
multirate capabilities present in the PHY layer of IEEE 802.11
WLANs.

CONGESTION-DISTORTION OPTIMIZED SCHEDULING

CoDiO determines which packets to send, and when, by comparing different possible
schedules in terms of their expected impact on video distortion and network
congestion. Forming the expected Lagrangian of expected end-to-end delay (the
metric we choose for congestion) and distortion (i.e. mean squared error), is better
suited than the traditional rate-distortion metric proposed in the seminal work, when
evaluating the performance of a sender operating over a throughput-limited network.
In particular, end-to-end delay depends on the capacity of the network path, reflects
time varying network conditions, and increases without bound as the sending rate
approaches capacity.

To find an optimized schedule, CoDiO selects the most important packets in terms of
video distortion reduction, and transmits them in an order which minimizes the
congestion created on the network. For example, I or SI frames are sent in priority
whereas some B frames might not be transmitted at all. In addition, CoDiO avoids
sending packets in large bursts as this has the worse effect on queuing delay.

Fig. 1. Video quality performance of different schedulers under varying latency constraints.

Figure 1 illustrates the benefits of CoDiO scheduling compared to a traditional ARQ
scheduler which sends packet sequentially and re-transmits lost packets based on out
of order acknowledgement, as long as they are not past their play out deadline. The
performance of a scheduler denoted “CoDiO Light” is also represented. This is a
low complexity CoDiO scheduler which determines sequentially which frame should
be transmitted to minimize the expected Lagrangian cost of congestion and
distortion, and spaces transmissions based on the time needed for the last frame to
traverse the bottleneck link. In the experiment, the video sequence Mother and
Daughter, encoded with H.264 at 270 kb/s, is sent over a 400 kb/s capacity path;
the one-way propagation delay is 50ms and the packet loss rate is 2%. In this
simulation, network paths are simulated in the network simulator NS-2.

Video quality is measured as the average PSNR measured in decibel and the
performance is shown for different maximum tolerated latencies, that is the time
between which a video frame is available at the server and the time at which it is
played out at the receiver. As shown in Fig. 1, the video quality of the three
schedulers is comparable when the latency constraint is relaxed, however, for
tight delay constraints, both optimized schedulers outperform by a significant
margin the ARQ scheduler. The results also indicate that the low-complexity

CoDiO scheduler achieves most of the gain. Hence, in the following, it is this
type of scheduler which will be extended to the P2P scenario, to reduce
computational complexity at each peer.